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Kai-Wei Chang

47 papers hereh-index 201.9k citations57 works total

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

author position
  • middle author32
  • last author12

Across the 44 of 47 papers where every author was matched, so the position is known.

fields
  • cs.CL32
  • cs.CV10
  • cs.LG4
  • cs.AI1
same name
  • Kai-Wei Chang — 162 papers, h 66
  • Kai-Wei Chang — 26 papers, h 13
  • Kai-Wei Chang — 18 papers, h 8
  • Kai-Wei Chang — 12 papers, h 7
  • Kai-Wei Chang — 12 papers, h 3
  • Kai-Wei Chang — 12 papers, h 11

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
20232026
most cited"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters

20 citations · 46 across the 42 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

Geometric Calibration of Supervised Fine-Tuning

Jianzhu Bao, Jia-Chen Gu, Qingyuan Liu +4

Recent studies investigate the weight geometry through its spectral structure, which characterizes how the weights transform different input directions. Within this view, supervise…

cs.LG2026

TaoBench: Do Automated Theorem Prover LLMs Generalize Beyond MathLib?

Alexander K Taylor, Junyi Zhang, Ethan Ji +10

Automated theorem proving (ATP) benchmarks largely consist of problems formalized in MathLib, so current ATP training and evaluation are heavily biased toward MathLib's definitiona…

cs.LG2025

MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Poisoning Attacks

Hyeonjeong Ha, Qiusi Zhan, Jeonghwan Kim +6

Retrieval-augmented generation (RAG) has become a common practice in multimodal large language models (MLLM) to enhance factual grounding and reduce hallucination. Yet, its relianc…

cs.LG2024★ 6 cited

On Prompt-Driven Safeguarding for Large Language Models

Chujie Zheng, Fan Yin, Hao Zhou +5

Prepending model inputs with safety prompts is a common practice for safeguarding large language models (LLMs) against queries with harmful intents. However, the underlying working…

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