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Articles by Jonathan Berant
MT-PingEval: Evaluating Multi-Turn Collaboration with Private Information Games
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Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors
Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors ICLR 2024 oralEveryoneRevisionsBibTeX Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
[2401.01879] Theoretical guarantees on the best-of-n alignment policy
[Submitted on 3 Jan 2024 ( v1 ), last revised 31 Jan 2025 (this version, v2)] Title:Theoretical guarantees on the best-of-n alignment policy View a PDF of the paper titled Theoretical guarantees on the best-of-n alignment policy, by Ahmad Beirami and Alekh Agarwal and Jonathan Berant and Alexander D'Amour and 1 other authors View PDF HTML (experimental) Abstract:A simple and effective method for the inference-time alignment of generative models is the best-of-$n$ policy, where $n$ samples are...
[2306.13421] Long-range Language Modeling with Self-retrieval
Retrieval-augmented language models (LMs) have received much attention recently. However, typically the retriever is not trained jointly as a native component of the LM, but added to an already-pretrained LM, which limits the ability of the LM and the retriever to adapt to one another.
ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding
arXiv:2305.14196 (cs) [Submitted on 23 May 2023 (v1), last revised 17 Dec 2023 (this version, v3)] Download PDF HTML (experimental) Comments: Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2305.14196 [cs.CL] (or arXiv:2305.14196v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2305.14196 Submission history From: Uri Shaham [ view email] [v1] Tue, 23 May 2023 16:15:31 UTC (7,298 KB) [v2]...
[2012.14913] Transformer Feed-Forward Layers Are Key-Value Memories
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[2112.06311] Weakly Supervised Text-to-SQL Parsing through Question Decomposition
[Submitted on 12 Dec 2021 ( v1 ), last revised 2 Aug 2024 (this version, v4)] Title:Weakly Supervised Text-to-SQL Parsing through Question Decomposition View a PDF of the paper titled Weakly Supervised Text-to-SQL Parsing through Question Decomposition, by Tomer Wolfson and 1 other authors View PDF HTML (experimental) Abstract:Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data.
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant document or context, and required very little general background. To investigate question answering with prior knowledge, we present COMMONSENSEQA: a challenging new dataset for commonsense question answering.
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