37 citations · 93 across the 25 of their papers we have counts for
12 papers · 1 filter
Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
Gabrielle Kaili-May Liu, Avi Caciularu, Gal Yona +2
Metacognition is a critical component of intelligence that describes the ability to monitor and regulate one's own cognitive processes. Yet LLMs exhibit systemic deficiencies in ke…
Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models
Dehai Min, Giovanni Vaccarino, Huiyi Chen +3
Large Reasoning Models (LRMs) achieve strong performance by generating long chains of thought (CoT), but often overthink, continuing to reason after a solution has already stabiliz…
Hallucinations Undermine Trust; Metacognition is a Way Forward
Gal Yona, Mor Geva, Yossi Matias
Despite significant strides in factual reliability, errors -- often termed hallucinations -- remain a major concern for generative AI, especially as LLMs are increasingly expected…
Empty Shelves or Lost Keys? Recall Is the Bottleneck for Parametric Factuality
Nitay Calderon, Eyal Ben-David, Zorik Gekhman +2
Standard factuality evaluations of LLMs treat all errors alike, obscuring whether failures arise from missing knowledge (empty shelves) or from limited access to encoded facts (los…
SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge
Lukas Haas, Gal Yona, Giovanni D'Antonio +2
We introduce SimpleQA Verified, a 1,000-prompt benchmark for evaluating Large Language Model (LLM) short-form factuality based on OpenAI's SimpleQA. It addresses critical limitatio…
MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs
Gabrielle Kaili-May Liu, Gal Yona, Avi Caciularu +3
A critical component in the trustworthiness of LLMs is reliable uncertainty communication, yet LLMs often use assertive language when conveying false claims, leading to over-relian…
Confidence Improves Self-Consistency in LLMs
Amir Taubenfeld, Tom Sheffer, Eran Ofek +4
Self-consistency decoding enhances LLMs' performance on reasoning tasks by sampling diverse reasoning paths and selecting the most frequent answer. However, it is computationally e…
Keep Guessing? When Considering Inference Scaling, Mind the Baselines
Gal Yona, Or Honovich, Omer Levy +1
Scaling inference compute in large language models (LLMs) through repeated sampling consistently increases the coverage (fraction of problems solved) as the number of samples incre…
Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?
Gal Yona, Roee Aharoni, Mor Geva
We posit that large language models (LLMs) should be capable of expressing their intrinsic uncertainty in natural language. For example, if the LLM is equally likely to output two…
Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
Zorik Gekhman, Gal Yona, Roee Aharoni +4
When large language models are aligned via supervised fine-tuning, they may encounter new factual information that was not acquired through pre-training. It is often conjectured th…
Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers
Gal Yona, Roee Aharoni, Mor Geva
Factual questions typically can be answered correctly at different levels of granularity. For example, both ``August 4, 1961'' and ``1961'' are correct answers to the question ``Wh…
Surfacing Biases in Large Language Models using Contrastive Input Decoding
Gal Yona, Or Honovich, Itay Laish +1
Ensuring that large language models (LMs) are fair, robust and useful requires an understanding of how different modifications to their inputs impact the model's behaviour. In the…