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Oren Kalinsky

4 accepted papers

2026

Knowing When to Quit: A Principled Framework for Dynamic Abstention in LLM Reasoning

ICML 2026poster

Large language models (LLMs) using chain-of-thought reasoning often waste substantial compute by producing long, incorrect responses. Abstention can mitigate this by withholding outputs unlikely to be correct. While most abstention methods decide to withhold outputs before or after generation, dynam…

Cited by 0SourceScholar
2025

ChaI-TeA: A Benchmark for Evaluating Autocompletion of Interactions with LLM-based Chatbots

NAACL 2025short

The rise of LLMs has deflected a growing portion of human-computer interactions towards LLM-based chatbots.The remarkable abilities of these models allow users to interact using long, diverse natural language text covering a wide range of topics and styles. Phrasing these messages is a time and effo…

2024

Towards Translating Objective Product Attributes Into Customer Language

NAACL 2024industry

When customers search online for a product they are not familiar with, their needs are often expressed through subjective product attributes, such as ”picture quality” for a TV or ”easy to clean” for a sofa. In contrast, the product catalog in online stores includes objective attributes such as ”scr…

Cited by 0SourcePDFScholar
2021

WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation

ACL 2021short

Recent works made significant advances on summarization tasks, facilitated by summarization datasets. Several existing datasets have the form of coherent-paragraph summaries. However, these datasets were curated from academic documents that were written for experts, thus making the essential step of…