3 citations · 5 across the 8 of their papers we have counts for
9 papers · 1 filter
What Models Know, How Well They Know It: Knowledge-Weighted Fine-Tuning for Learning When to Say "I Don't Know"
Joosung Lee, Hwiyeol Jo, Donghyeon Ko +3
While large language models (LLMs) demonstrate strong capabilities across diverse user queries, they still suffer from hallucinations, often arising from knowledge misalignment bet…
OmniACBench: A Benchmark for Evaluating Context-Grounded Acoustic Control in Omni-Modal Models
Seunghee Kim, Bumkyu Park, Kyudan Jung +5
Most testbeds for omni-modal models assess multimodal understanding via textual outputs, leaving it unclear whether these models can properly speak their answers. To study this, we…
Finding Answers in Thought Matters: Revisiting Evaluation on Large Language Models with Reasoning
Hwiyeol Jo, Joosung Lee, Jaehone Lee +3
Evaluating generative models, such as large language models (LLMs), commonly involves question-answering tasks where the final answer is selected based on probability of answer cho…
Enhancing Hallucination Detection via Future Context
Joosung Lee, Cheonbok Park, Hwiyeol Jo +3
Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process. As users increasingly encounter such black-bo…
Enhanced Facet Generation with LLM Editing
Joosung Lee, Jinhong Kim
In information retrieval, facet identification of a user query is an important task. If a search service can recognize the facets of a user's query, it has the potential to offer u…
P5: Plug-and-Play Persona Prompting for Personalized Response Selection
Joosung Lee, Minsik Oh, Donghun Lee
The use of persona-grounded retrieval-based chatbots is crucial for personalized conversations, but there are several challenges that need to be addressed. 1) In general, collectin…