13 citations · 30 across the 9 of their papers we have counts for
9 papers
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…
A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains
Alon Jacovi, Yonatan Bitton, Bernd Bohnet +6
Prompting language models to provide step-by-step answers (e.g., "Chain-of-Thought") is the prominent approach for complex reasoning tasks, where more accurate reasoning chains typ…
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…
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
Or Honovich, Thomas Scialom, Omer Levy +1
Instruction tuning enables pretrained language models to perform new tasks from inference-time natural language descriptions. These approaches rely on vast amounts of human supervi…
LMentry: A Language Model Benchmark of Elementary Language Tasks
Avia Efrat, Or Honovich, Omer Levy
As the performance of large language models rapidly improves, benchmarks are getting larger and more complex as well. We present LMentry, a benchmark that avoids this "arms race" b…
DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering
Ella Neeman, Roee Aharoni, Or Honovich +3
Question answering models commonly have access to two sources of "knowledge" during inference time: (1) parametric knowledge - the factual knowledge encoded in the model weights, a…
Instruction Induction: From Few Examples to Natural Language Task Descriptions
Or Honovich, Uri Shaham, Samuel R. Bowman +1
Large language models are able to perform a task by conditioning on a few input-output demonstrations - a paradigm known as in-context learning. We show that language models can ex…
TRUE: Re-evaluating Factual Consistency Evaluation
Or Honovich, Roee Aharoni, Jonathan Herzig +7
Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability. Automatic factual consistency evaluation may h…
: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering
Or Honovich, Leshem Choshen, Roee Aharoni +3
Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting t…