Papers by Roi Cohen
LM vs LM: Detecting Factual Errors via Cross Examination (2023.emnlp-main)
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| Challenge: | Modern language models (LMs) generate inconsistent, non-attributable or factually incorrect text, which hinders their usability. |
| Approach: | They propose a factuality evaluation framework for LMs that is based on cross-examination to detect inconsistencies between LM and examiner. |
| Outcome: | The proposed framework outperforms existing methods and baselines on factual claims on four benchmarks. |
Crawling The Internal Knowledge-Base of Language Models (2023.findings-eacl)
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| Challenge: | Existing methods for representing factual knowledge in a language model are insufficient. |
| Approach: | They propose a procedure for “crawling” the internal knowledge-base of a language model by expanding a knowledge-graph around it. |
| Outcome: | The proposed method yields high precision graphs (82-92%) while emitting a reasonable number of facts per entity. |
InFact: Informativeness Alignment for Improved LLM Factuality (2025.findings-emnlp)
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| Challenge: | despite factual errors, LLMs tend to generate factual text that is factually correct but less informative than other, more informative choices. |
| Approach: | They propose an objective that prioritizes answers that are both correct and informative . |
| Outcome: | a new mechanism prioritizes correct and informative answers based on factual benchmarks . the proposed model improves both accuracy and factuality by maximizing the objective . |