# Leshem Choshen

Postdoctoral Researcher at MIT-IBM Watson AI Lab, IBM Research, Weizmann Institute of Science.
ORCID 0000-0002-0085-6496 · leshem.choshen@weizmann.ac.il

Each paper below has a plain-text page stating what it shows, the conditions the
claim holds under, and common misreadings -- written by the author, not extracted.

## Papers
- [TIES-Merging: Resolving Interference When Merging Models](https://borgr.github.io/papers/ties-merging-resolving-interference-when-merging-models/llms.txt) — TIES-Merging combines several independently fine-tuned models into one without retraining, by discarding small parameter changes and resolving sign conflicts before averaging.
- [tinyBenchmarks: evaluating LLMs with fewer examples](https://borgr.github.io/papers/tinybenchmarks-evaluating-llms-with-fewer-examples/llms.txt)
- [Active Learning for BERT: An Empirical Study](https://borgr.github.io/papers/active-learning-for-bert-an-empirical-study/llms.txt)
- [Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora](https://borgr.github.io/papers/findings-of-the-b-aby-lm-challenge-sample-efficient-pretrain/llms.txt)
- [Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation](https://borgr.github.io/papers/global-mmlu-understanding-and-addressing-cultural-and-lingui/llms.txt)
- [An autonomous debating system](https://borgr.github.io/papers/an-autonomous-debating-system/llms.txt)
- [Q²: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering](https://borgr.github.io/papers/q-2-evaluating-factual-consistency-in-knowledge-grounded-dia/llms.txt)
- [On the Weaknesses of Reinforcement Learning for Neural Machine Translation](https://borgr.github.io/papers/on-the-weaknesses-of-reinforcement-learning-for-neural-machi/llms.txt)
- [Fusing finetuned models for better pretraining](https://borgr.github.io/papers/fusing-finetuned-models-for-better-pretraining/llms.txt)
- [DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering](https://borgr.github.io/papers/disentqa-disentangling-parametric-and-contextual-knowledge-w/llms.txt)
- [Model merging with SVD to tie the Knots](https://borgr.github.io/papers/model-merging-with-svd-to-tie-the-knots/llms.txt)
- [Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty](https://borgr.github.io/papers/beyond-binary-rewards-training-lm-s-to-reason-about-their-un/llms.txt)
- [Jump to Conclusions: Short-Cutting Transformers with Linear Transformations](https://borgr.github.io/papers/jump-to-conclusions-short-cutting-transformers-with-linear-t/llms.txt)
- [Asymmetry in Low-Rank Adapters of Foundation Models](https://borgr.github.io/papers/asymmetry-in-low-rank-adapters-of-foundation-models/llms.txt)
- [Efficient multi-prompt evaluation of LLMs](https://borgr.github.io/papers/efficient-multi-prompt-evaluation-of-llms/llms.txt)
- [Call for Papers - The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus](https://borgr.github.io/papers/call-for-papers-the-babylm-challenge-sample-efficient-pretra/llms.txt)
- [Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network](https://borgr.github.io/papers/are-you-convinced-choosing-the-more-convincing-evidence-with/llms.txt)
- [Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining](https://borgr.github.io/papers/will-it-blend-blending-weak-and-strong-labeled-data-in-a-neu/llms.txt)
- [Knowledge is a Region in Weight Space for Fine-tuned Language Models](https://borgr.github.io/papers/knowledge-is-a-region-in-weight-space-for-fine-tuned-languag/llms.txt)
- [Corpus Wide Argument Mining - A Working Solution](https://borgr.github.io/papers/corpus-wide-argument-mining-a-working-solution/llms.txt)
- [DORA The Explorer: Directed Outreaching Reinforcement Action-Selection](https://borgr.github.io/papers/dora-the-explorer-directed-outreaching-reinforcement-action/llms.txt)
- [A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning](https://borgr.github.io/papers/a-survey-on-model-moerging-recycling-and-routing-among-speci/llms.txt)
- [Efficient Benchmarking (of Language Models)](https://borgr.github.io/papers/efficient-benchmarking-of-language-models/llms.txt)
- [Elements of World Knowledge (EWOK): A cognition-inspired framework for evaluating basic world knowledge in language models](https://borgr.github.io/papers/elements-of-world-knowledge-ewok-a-cognition-inspired-framew/llms.txt)
- [ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning](https://borgr.github.io/papers/cold-fusion-collaborative-descent-for-distributed-multitask/llms.txt)
- [Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets](https://borgr.github.io/papers/let-s-agree-to-agree-neural-networks-share-classification-or/llms.txt)
- [Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora](https://borgr.github.io/papers/findings-of-the-second-b-aby-lm-challenge-sample-efficient-p/llms.txt)
- [NumeroLogic: Number Encoding for Enhanced LLMs' Numerical Reasoning](https://borgr.github.io/papers/n-umero-l-ogic-number-encoding-for-enhanced-llm-s-numerical/llms.txt)
- [The Grammar-Learning Trajectories of Neural Language Models](https://borgr.github.io/papers/the-grammar-learning-trajectories-of-neural-language-models/llms.txt)
- [[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus](https://borgr.github.io/papers/call-for-papers-the-2nd-babylm-challenge-sample-efficient-pr/llms.txt)
- [SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA](https://borgr.github.io/papers/semeval-2019-task-1-cross-lingual-semantic-parsing-with-ucca/llms.txt)
- [Inherent Biases in Reference-based Evaluation for Grammatical Error Correction](https://borgr.github.io/papers/inherent-biases-in-reference-based-evaluation-for-grammatica-4eccd3/llms.txt)
- [Genie: Achieving Human Parity in Content-Grounded Datasets Generation](https://borgr.github.io/papers/genie-achieving-human-parity-in-content-grounded-datasets-ge/llms.txt)
- [Reference-less Measure of Faithfulness for Grammatical Error Correction](https://borgr.github.io/papers/reference-less-measure-of-faithfulness-for-grammatical-error/llms.txt)
- [Automatic Metric Validation for Grammatical Error Correction](https://borgr.github.io/papers/automatic-metric-validation-for-grammatical-error-correction/llms.txt)
- [BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop](https://borgr.github.io/papers/babylm-turns-3-call-for-papers-for-the-2025-babylm-workshop/llms.txt)
- [Learning to combine Grammatical Error Corrections](https://borgr.github.io/papers/learning-to-combine-grammatical-error-corrections/llms.txt)
- [Bigger is not always better: The importance of human-scale language modeling for psycholinguistics](https://borgr.github.io/papers/bigger-is-not-always-better-the-importance-of-human-scale-la/llms.txt)
- [Where to start? Analyzing the potential value of intermediate models](https://borgr.github.io/papers/where-to-start-analyzing-the-potential-value-of-intermediate/llms.txt)
- [Cluster & Tune: Boost Cold Start Performance in Text Classification](https://borgr.github.io/papers/cluster-tune-boost-cold-start-performance-in-text-classifica/llms.txt)
- [Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead](https://borgr.github.io/papers/compress-then-serve-serving-thousands-of-lo-ra-adapters-with/llms.txt)
- [Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability](https://borgr.github.io/papers/deductive-closure-training-of-language-models-for-coherence/llms.txt)
- [Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families](https://borgr.github.io/papers/sloth-scaling-laws-for-llm-skills-to-predict-multi-benchmark/llms.txt)
- [The Language of Legal and Illegal Activity on the Darknet](https://borgr.github.io/papers/the-language-of-legal-and-illegal-activity-on-the-darknet/llms.txt)
- [Data Contamination Report from the 2024 CONDA Shared Task](https://borgr.github.io/papers/data-contamination-report-from-the-2024-conda-shared-task/llms.txt)
- [Classifying Syntactic Errors in Learner Language](https://borgr.github.io/papers/classifying-syntactic-errors-in-learner-language/llms.txt)
- [A Hitchhiker's Guide to Scaling Law Estimation](https://borgr.github.io/papers/a-hitchhiker-s-guide-to-scaling-law-estimation/llms.txt)
- [Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI](https://borgr.github.io/papers/unitxt-flexible-shareable-and-reusable-data-preparation-and/llms.txt)
- [DOVE: A Large-Scale Multi-Dimensional Predictions Dataset Towards Meaningful LLM Evaluation](https://borgr.github.io/papers/dove-a-large-scale-multi-dimensional-predictions-dataset-tow/llms.txt)
- [ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization](https://borgr.github.io/papers/com-peft-compression-for-communicating-parameter-efficient-u/llms.txt)
- [Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBench](https://borgr.github.io/papers/do-these-llm-benchmarks-agree-fixing-benchmark-evaluation-wi/llms.txt)
- [Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours](https://borgr.github.io/papers/label-sleuth-from-unlabeled-text-to-a-classifier-in-a-few-ho/llms.txt)
- [ZipNN: Lossless Compression for AI Models](https://borgr.github.io/papers/zipnn-lossless-compression-for-ai-models/llms.txt)
- [Automatically Extracting Challenge Sets for Non-Local Phenomena in Neural Machine Translation](https://borgr.github.io/papers/automatically-extracting-challenge-sets-for-non-local-phenom/llms.txt)
- [When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation](https://borgr.github.io/papers/when-ai-benchmarks-plateau-a-systematic-study-of-benchmark-s/llms.txt)
- [LiveXiv - A Multi-Modal live benchmark based on Arxiv papers content](https://borgr.github.io/papers/livexiv-a-multi-modal-live-benchmark-based-on-arxiv-papers-c/llms.txt)
- [Beneath the Surface of Consistency: Exploring Cross-lingual Knowledge Representation Sharing in LLMs](https://borgr.github.io/papers/beneath-the-surface-of-consistency-exploring-cross-lingual-k/llms.txt)
- [Label-Efficient Model Selection for Text Generation](https://borgr.github.io/papers/label-efficient-model-selection-for-text-generation/llms.txt)
- [Human Learning by Model Feedback: The Dynamics of Iterative Prompting with Midjourney](https://borgr.github.io/papers/human-learning-by-model-feedback-the-dynamics-of-iterative-p/llms.txt)
- [Fuse to Forget: Bias Reduction and Selective Memorization through Model Fusion](https://borgr.github.io/papers/fuse-to-forget-bias-reduction-and-selective-memorization-thr/llms.txt)
- [Mediators in Determining what Processing BERT Performs First](https://borgr.github.io/papers/mediators-in-determining-what-processing-bert-performs-first/llms.txt)
- [The Future of Open Human Feedback](https://borgr.github.io/papers/the-future-of-open-human-feedback/llms.txt)
- [Lossless and Near-Lossless Compression for Foundation Models](https://borgr.github.io/papers/lossless-and-near-lossless-compression-for-foundation-models/llms.txt)
- [Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains](https://borgr.github.io/papers/unsupervised-expressive-rules-provide-explainability-and-ass/llms.txt)
- [Naturally Occurring Feedback is Common, Extractable and Useful](https://borgr.github.io/papers/naturally-occurring-feedback-is-common-extractable-and-usefu/llms.txt)
- [The Mighty ToRR: A Benchmark for Table Reasoning and Robustness](https://borgr.github.io/papers/the-mighty-torr-a-benchmark-for-table-reasoning-and-robustne/llms.txt)
- [Semantics-aware Attention Improves Neural Machine Translation](https://borgr.github.io/papers/semantics-aware-attention-improves-neural-machine-translatio/llms.txt)
- [Findings of the Third BabyLM Challenge: Accelerating Language Modeling Research with Cognitively Plausible Data](https://borgr.github.io/papers/findings-of-the-third-b-aby-lm-challenge-accelerating-langua/llms.txt)
- [PreQuEL: Quality Estimation of Machine Translation Outputs in Advance](https://borgr.github.io/papers/prequel-quality-estimation-of-machine-translation-outputs-in/llms.txt)
- [SERRANT: a syntactic classifier for English Grammatical Error Types](https://borgr.github.io/papers/serrant-a-syntactic-classifier-for-english-grammatical-error/llms.txt)
- [The ShareLM Collection and Plugin: Contributing Human-Model Chats for the Benefit of the Community](https://borgr.github.io/papers/the-s-hare-lm-collection-and-plugin-contributing-human-model/llms.txt)
- [Benchmark Agreement Testing Done Right: A Guide for LLM Benchmark Evaluation](https://borgr.github.io/papers/benchmark-agreement-testing-done-right-a-guide-for-llm-bench/llms.txt)
- [Global PIQA: Evaluating Physical Commonsense Reasoning Across 100+ Languages and Cultures](https://borgr.github.io/papers/global-piqa-evaluating-physical-commonsense-reasoning-across/llms.txt)
- [Neurips 2023 llm efficiency fine-tuning competition](https://borgr.github.io/papers/neurips-2023-llm-efficiency-fine-tuning-competition/llms.txt)
- [Navigating the Modern Evaluation Landscape: Considerations in Benchmarks and Frameworks for Large Language Models (LLMs)](https://borgr.github.io/papers/navigating-the-modern-evaluation-landscape-considerations-in/llms.txt)
- [Reinforcement Learning with Large Action Spaces for Neural Machine Translation](https://borgr.github.io/papers/reinforcement-learning-with-large-action-spaces-for-neural-m/llms.txt)
- [GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns](https://borgr.github.io/papers/grasp-a-library-for-extracting-and-exploring-human-interpret/llms.txt)
- [Transition based Graph Decoder for Neural Machine Translation](https://borgr.github.io/papers/transition-based-graph-decoder-for-neural-machine-translatio/llms.txt)
- [CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data](https://borgr.github.io/papers/commonlid-re-evaluating-state-of-the-art-language-identifica/llms.txt)
- [TextArena](https://borgr.github.io/papers/textarena/llms.txt)
- [Holmes: Benchmark the Linguistic Competence of Language Models](https://borgr.github.io/papers/holmes-benchmark-the-linguistic-competence-of-language-model/llms.txt)
- [General Agent Evaluation](https://borgr.github.io/papers/general-agent-evaluation/llms.txt)
- [ComSum: Commit Messages Summarization and Meaning Preservation](https://borgr.github.io/papers/comsum-commit-messages-summarization-and-meaning-preservatio/llms.txt)
- [ErrorMap and ErrorAtlas: Charting the Failure Landscape of Large Language Models](https://borgr.github.io/papers/errormap-and-erroratlas-charting-the-failure-landscape-of-la/llms.txt)
- [Pretraining Language Models for Diachronic Linguistic Change Discovery](https://borgr.github.io/papers/pretraining-language-models-for-diachronic-linguistic-change/llms.txt)
- [Do LLMs Benefit From Their Own Words?](https://borgr.github.io/papers/do-llms-benefit-from-their-own-words/llms.txt)
- [BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data](https://borgr.github.io/papers/babybabellm-a-multilingual-benchmark-of-developmentally-plau/llms.txt)
- [Unforgettable Generalization in Language Models](https://borgr.github.io/papers/unforgettable-generalization-in-language-models/llms.txt)
- [MINDGAMES: A Live Arena for Evaluating Social and Strategic Reasoning in Multi-Agent LLMs](https://borgr.github.io/papers/mindgames-a-live-arena-for-evaluating-social-and-strategic-r/llms.txt)
- [CUBE: A Standard for Unifying Agent Benchmarks](https://borgr.github.io/papers/cube-a-standard-for-unifying-agent-benchmarks/llms.txt)
- [From KMMLU-Redux to Pro: A Professional Korean Benchmark Suite for LLM Evaluation](https://borgr.github.io/papers/from-kmmlu-redux-to-pro-a-professional-korean-benchmark-suit/llms.txt)
- [Robustness as an Emergent Property of Task Performance](https://borgr.github.io/papers/robustness-as-an-emergent-property-of-task-performance/llms.txt)
- [Will it Merge? On The Causes of Model Mergeability](https://borgr.github.io/papers/will-it-merge-on-the-causes-of-model-mergeability/llms.txt)
- [Mediocrity is the key for LLM as a Judge Anchor Selection](https://borgr.github.io/papers/mediocrity-is-the-key-for-llm-as-a-judge-anchor-selection/llms.txt)
- [LLM Hypnosis: Exploiting User Feedback for Unauthorized Knowledge Injection to All Users](https://borgr.github.io/papers/llm-hypnosis-exploiting-user-feedback-for-unauthorized-knowl/llms.txt)
- [On Neurons Invariant to Sentence Structural Changes in Neural Machine Translation](https://borgr.github.io/papers/on-neurons-invariant-to-sentence-structural-changes-in-neura/llms.txt)
- [SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation](https://borgr.github.io/papers/semeval-2019-shared-task-cross-lingual-semantic-parsing-with/llms.txt)
- [BabyLM Turns 4 and Goes Multilingual: Call for Papers for the 2026 BabyLM Workshop](https://borgr.github.io/papers/babylm-turns-4-and-goes-multilingual-call-for-papers-for-the/llms.txt)
- [Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Results](https://borgr.github.io/papers/every-eval-ever-a-unifying-schema-and-community-repository-f/llms.txt)
- [Automated Discovery Has No Universally Superior Harness](https://borgr.github.io/papers/automated-discovery-has-no-universally-superior-harness/llms.txt)
- [How Safe is Your Safety Metric? Automatic Concatenation Tests for Metric Reliability](https://borgr.github.io/papers/how-safe-is-your-safety-metric-automatic-concatenation-tests/llms.txt)
- [A Latent Variable Framework for Scaling Laws in Large Language Models](https://borgr.github.io/papers/a-latent-variable-framework-for-scaling-laws-in-large-langua/llms.txt)
- [Insights from the first BabyLM Challenge: Training sample-efficient language models on a developmentally plausible corpus](https://borgr.github.io/papers/insights-from-the-first-babylm-challenge-training-sample-eff/llms.txt)
- [Enhancing the Transformer Decoder with Transition-based Syntax](https://borgr.github.io/papers/enhancing-the-transformer-decoder-with-transition-based-synt/llms.txt)
- [Part of Speech and Universal Dependency effects on English Arabic Machine Translation](https://borgr.github.io/papers/part-of-speech-and-universal-dependency-effects-on-english-a/llms.txt)
- [All Neural Networks are Created Equal](https://borgr.github.io/papers/all-neural-networks-are-created-equal/llms.txt)
- [Position: Agentic Systems Should be General](https://borgr.github.io/papers/position-agentic-systems-should-be-general/llms.txt)
- [Cross-Lingual Exploration for Parametric Knowledge](https://borgr.github.io/papers/cross-lingual-exploration-for-parametric-knowledge/llms.txt)
- [Instructions Shape Production of Language, not Processing](https://borgr.github.io/papers/instructions-shape-production-of-language-not-processing/llms.txt)
- [Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting](https://borgr.github.io/papers/evaluation-cards-an-interpretive-layer-for-ai-evaluation-rep/llms.txt)
- [Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data](https://borgr.github.io/papers/stop-guessing-when-to-stop-testing-efficient-model-evaluatio/llms.txt)
- [Resolving Interference (RI): Disentangling Models for Improved Model Merging](https://borgr.github.io/papers/resolving-interference-ri-disentangling-models-for-improved/llms.txt)
- [CRISP: Complex Reasoning with Interpretable Step-based Plans](https://borgr.github.io/papers/crisp-complex-reasoning-with-interpretable-step-based-plans/llms.txt)
- [Can Gradient Descent Simulate Prompting?](https://borgr.github.io/papers/can-gradient-descent-simulate-prompting/llms.txt)
- [Tie the KnOTS: Model Merging with SVD](https://borgr.github.io/papers/tie-the-knots-model-merging-with-svd/llms.txt)
- [LLM Merging: Building LLMs Efficiently through Merging](https://borgr.github.io/papers/llm-merging-building-llm-s-efficiently-through-merging/llms.txt)
- [DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging](https://borgr.github.io/papers/denseformer-enhancing-information-flow-in-transformers-via-d/llms.txt)
- [High-dimensional Learning Dynamics 2024: The Emergence of Structure and Reasoning Workshop at ICML24](https://borgr.github.io/papers/high-dimensional-learning-dynamics-2024-the-emergence-of-str/llms.txt)
- [Super Tiny Language Models](https://borgr.github.io/papers/super-tiny-language-models/llms.txt)
- [A framework for few-shot language model evaluation](https://borgr.github.io/papers/a-framework-for-few-shot-language-model-evaluation/llms.txt)
- [The Heuristic Core: Understanding Subnetwork Generalization in Pretrained Language Models](https://borgr.github.io/papers/the-heuristic-core-understanding-subnetwork-generalization-i/llms.txt)
- [Can You Trust Your Metric? Automatic Concatenation-Based Tests for Metric Validity](https://borgr.github.io/papers/can-you-trust-your-metric-automatic-concatenation-based-test/llms.txt)
- [The llama 3 herd of models](https://borgr.github.io/papers/the-llama-3-herd-of-models/llms.txt)
- [Not all layers are equally as important: Every Layer Counts BERT](https://borgr.github.io/papers/not-all-layers-are-equally-as-important-every-layer-counts-b/llms.txt)
- [MuLER: Detailed and Scalable Reference-based Evaluation](https://borgr.github.io/papers/muler-detailed-and-scalable-reference-based-evaluation/llms.txt)
- [Resolving Interference When Merging Models](https://borgr.github.io/papers/resolving-interference-when-merging-models/llms.txt)
- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://borgr.github.io/papers/model-soups-averaging-weights-of-multiple-fine-tuned-models/llms.txt)
- [Some Grammatical Errors are Frequent, Others are Important](https://borgr.github.io/papers/some-grammatical-errors-are-frequent-others-are-important/llms.txt)
- [Holistic evaluation of language models](https://borgr.github.io/papers/holistic-evaluation-of-language-models/llms.txt)
- [Merging models with fisher-weighted averaging](https://borgr.github.io/papers/merging-models-with-fisher-weighted-averaging/llms.txt)
- [Inherent Biases in Reference-based Evaluation for Grammatical Error Correction and Text Simplification](https://borgr.github.io/papers/inherent-biases-in-reference-based-evaluation-for-grammatica-2018/llms.txt)
- [Attention is all you need](https://borgr.github.io/papers/attention-is-all-you-need/llms.txt)
- [Mapping the early language environment using all-day recordings and automated analysis](https://borgr.github.io/papers/mapping-the-early-language-environment-using-all-day-recordi/llms.txt)
- [Sapiens: A brief history of humankind](https://borgr.github.io/papers/sapiens-a-brief-history-of-humankind/llms.txt)
- [European public acceptance of euthanasia: socio-demographic and cultural factors associated with the acceptance of euthanasia in 33 European countries](https://borgr.github.io/papers/european-public-acceptance-of-euthanasia-socio-demographic-a/llms.txt)

## Guides
- [arXiv_stuck](https://github.com/borgr/arXiv_stuck) — An arXiv moderator's explanation of why submissions get held, stuck, or rejected.
- [facultips](https://github.com/borgr/facultips) — A guide to applying for tenure-track faculty positions: research and teaching statements, recommendations, and job talks.
- [paper_updated](https://github.com/borgr/paper_updated) — A curated list of ways to keep up with newly published research papers.
- [post](https://github.com/borgr/post) — A guide to finding and applying for a good postdoc position.
- [tutEval](https://github.com/borgr/tutEval) — Materials for the LREC-COLING 2024 tutorial on evaluating large language models: benchmarks, prompts, metrics, and manual evaluation.