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Benjamin Newman

6 accepted papers

2026

Spectrum Tuning: Post-Training for Distributional Coverage and In-Context Steerability

ICLR 2026poster

Language model post-training has enhanced instruction-following and performance on many downstream tasks, but also comes with an often-overlooked cost on tasks with many possible valid answers. We characterize three desiderata: in-context steerability, valid output space coverage, and distributional…

Cited by 0SourcecodeScholar
2024

ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models

EMNLP 2024main

When conducting literature reviews, scientists often create literature review tables—tables whose rows are publications and whose columns constitute a schema, a set of aspects used to compare and contrast the papers. Can we automatically generate these tables using language models (LMs)? In this wor…

Cited by 5SourcePDFScholar
2024

The Generative AI Paradox: “What It Can Create, It May Not Understand”

ICLR 2024poster

The recent wave of generative AI has sparked unprecedented global attention, with both excitement and concern over potentially superhuman levels of artificial intelligence: models now take only seconds to produce outputs that would challenge or exceed the capabilities even of expert humans. At the s…

Cited by 30SourcePDFScholar
2023

A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents

EMNLP 2023long main

Many real-world applications (e.g., note taking, search) require extracting a sentence or paragraph from a document and showing that snippet to a human outside of the source document. Yet, users may find snippets difficult to understand as they lack context from the original document. In this work,…

Cited by 0SourceScholar
2022

P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts

ICLR 2022poster

Recent work (e.g. LAMA (Petroni et al., 2019)) has found that the quality of the factual information extracted from Large Language Models (LLMs) depends on the prompts used to query them. This inconsistency is problematic because different users will query LLMs for the same information using differe…

2021

Refining Targeted Syntactic Evaluation of Language Models

NAACL 2021long

Targeted syntactic evaluation of subject-verb number agreement in English (TSE) evaluates language models’ syntactic knowledge using hand-crafted minimal pairs of sentences that differ only in the main verb’s conjugation. The method evaluates whether language models rate each grammatical sentence as…