Papers by Gal Yona
Confidence Improves Self-Consistency in LLMs (2025.findings-acl)
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| Challenge: | Modern large language models (LLMs) demonstrate strong reasoning capabilities, driven in part by their capacity to generate a sequence of intermediate reasoning steps that lead them toward a final answer. |
| Approach: | They propose a method that performs a weighted majority vote based on confidence scores obtained directly from the model. |
| Outcome: | The proposed method outperforms self-consistency on nine models and four datasets, reducing the required number of reasoning paths by over 40% on average. |
Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words? (2024.emnlp-main)
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| Challenge: | Despite their unprecedented capabilities, large language models (LLMs) often output erroneous information, which may lead users to overly rely on their false output. |
| Approach: | They formalize faithful response uncertainty based on the gap between the model’s intrinsic confidence in the assertions it makes and the decisiveness by which they are conveyed. |
| Outcome: | The proposed model is poor at faithfully conveying uncertainty on knowledge-intensive questions. |
Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers (2024.acl-long)
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| Challenge: | Standard question answering evaluation protocols do not take factual errors into account and compare a predicted answer against answers of a single granularity level. |
| Approach: | They propose a new evaluation setting where a predicted answer is evaluated in terms of accuracy and informativeness against a set of multi-granularity answers. |
| Outcome: | The proposed evaluation setting compares a predicted answer against a set of multi-granularity answers. |
MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs (2025.emnlp-main)
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| Challenge: | Existing methods for faithful calibration of large language models (LLMs) are insufficient and can harm faithful calibration. |
| Approach: | They propose a new prompt-based calibration approach inspired by human metacognition that measures faithfulness across diverse models and task domains and enables up to 61% improvement in faithfulness. |
| Outcome: | The proposed approach improves faithfulness across diverse models and task domains and achieves an 83% win rate over original generations as judged by humans. |
Keep Guessing? When Considering Inference Scaling, Mind the Baselines (2025.findings-naacl)
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| Challenge: | Experiments spanning mathematical reasoning and factual knowledge reveal that repeated sampling improves coverage in large language models. |
| Approach: | They define a baseline that enumerates answers according to their prevalence in the training set and then scale it to measure coverage. |
| Outcome: | The proposed model outperforms repeated model sampling for some LLMs while the coverage for others is on par with that of a mixture strategy that obtains k answers by using only 10 model samples and guessing the remaining k-10 attempts via enumeration. |
Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations? (2024.emnlp-main)
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| Challenge: | Pre-training Large Language Models (LLMs) on textual corpora embeds substantial factual knowledge in their parameters, which is essential for excelling in various downstream applications. |
| Approach: | They propose to use supervised fine-tuning to align large language models to new factual information that is not acquired through pre-training. |
| Outcome: | The proposed model is trained to generate facts that are not grounded in pre-existing knowledge, but hallucinates when examples with new knowledge are learned. |