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Jixuan Leng

University of Rochester

7 papers hereh-index 5207 citations11 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author3

Across the 7 of 7 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.LG2
  • cs.CV1
affiliations
  • University of Rochester
  • Carnegie Mellon University
Homepage
same name
  • Jixuan Leng — 2 papers, h 2
  • Jixuan Leng — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232025
most citedTaming Overconfidence in LLMs: Reward Calibration in RLHF

3 citations · 4 across the 6 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2025

POSS: Position Specialist Generates Better Draft for Speculative Decoding

Langlin Huang, Chengsong Huang, Jixuan Leng +2

Speculative decoding accelerates Large Language Model (LLM) inference by using a small draft model to predict multiple tokens, and a large target model to verify these tokens in pa…

cs.CL2025

Semi-structured LLM Reasoners Can Be Rigorously Audited

Jixuan Leng, Cassandra A. Cohen, Zhixian Zhang +2

Although Large Language Models (LLMs) have become capable reasoners, the problem of faithfulness persists: their reasoning can contain errors and omissions that are difficult to de…

cs.CL2025

CrossWordBench: Evaluating the Reasoning Capabilities of LLMs and LVLMs with Controllable Puzzle Generation

Jixuan Leng, Chengsong Huang, Langlin Huang +4

Existing reasoning evaluation frameworks for Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) predominantly assess either text-based reasoning or vision-langua…

cs.CL2024★ 3 cited

Taming Overconfidence in LLMs: Reward Calibration in RLHF

Jixuan Leng, Chengsong Huang, Banghua Zhu +1

Language model calibration refers to the alignment between the confidence of the model and the actual performance of its responses. While previous studies point out the overconfide…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.