32 citations · 174 across the 23 of their papers we have counts for
23 papers
PromptDistill: Query-based Selective Token Retention in Intermediate Layers for Efficient Large Language Model Inference
Weisheng Jin, Maojia Song, Tej Deep Pala +4
As large language models (LLMs) tackle increasingly complex tasks and longer documents, their computational and memory costs during inference become a major bottleneck. To address…
The Jumping Reasoning Curve? Tracking the Evolution of Reasoning Performance in GPT-[n] and o-[n] Models on Multimodal Puzzles
Vernon Y. H. Toh, Yew Ken Chia, Deepanway Ghosal +1
The releases of OpenAI's o-[n] series, such as o1, o3, and o4-mini, mark a significant paradigm shift in Large Language Models towards advanced reasoning capabilities. Notably, mod…
M-Longdoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework
Yew Ken Chia, Liying Cheng, Hou Pong Chan +5
The ability to understand and answer questions over documents can be useful in many business and practical applications. However, documents often contain lengthy and diverse multim…
Reasoning Paths Optimization: Learning to Reason and Explore From Diverse Paths
Yew Ken Chia, Guizhen Chen, Weiwen Xu +3
Advanced models such as OpenAI o1 exhibit impressive problem-solving capabilities through step-by-step reasoning. However, they may still falter on more complex problems, making er…
Can-Do! A Dataset and Neuro-Symbolic Grounded Framework for Embodied Planning with Large Multimodal Models
Yew Ken Chia, Qi Sun, Lidong Bing +1
Large multimodal models have demonstrated impressive problem-solving abilities in vision and language tasks, and have the potential to encode extensive world knowledge. However, it…
SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages
Wenxuan Zhang, Hou Pong Chan, Yiran Zhao +9
Large Language Models (LLMs) have shown remarkable abilities across various tasks, yet their development has predominantly centered on high-resource languages like English and Chin…
Auto-Arena: Automating LLM Evaluations with Agent Peer Battles and Committee Discussions
Ruochen Zhao, Wenxuan Zhang, Yew Ken Chia +3
As LLMs continuously evolve, there is an urgent need for a reliable evaluation method that delivers trustworthy results promptly. Currently, static benchmarks suffer from inflexibi…
PuzzleVQA: Diagnosing Multimodal Reasoning Challenges of Language Models with Abstract Visual Patterns
Yew Ken Chia, Vernon Toh Yan Han, Deepanway Ghosal +2
Large multimodal models extend the impressive capabilities of large language models by integrating multimodal understanding abilities. However, it is not clear how they can emulate…
Contrastive Chain-of-Thought Prompting
Yew Ken Chia, Guizhen Chen, Luu Anh Tuan +2
Despite the success of chain of thought in enhancing language model reasoning, the underlying process remains less well understood. Although logically sound reasoning appears inher…
Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-Tuning
Deepanway Ghosal, Yew Ken Chia, Navonil Majumder +1
Recently, the release of INSTRUCTEVAL has provided valuable insights into the performance of large language models (LLMs) that utilize encoder-decoder or decoder-only architecture.…
INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models
Yew Ken Chia, Pengfei Hong, Lidong Bing +1
Instruction-tuned large language models have revolutionized natural language processing and have shown great potential in applications such as conversational agents. These models,…
M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models
Wenxuan Zhang, Sharifah Mahani Aljunied, Chang Gao +2
Despite the existence of various benchmarks for evaluating natural language processing models, we argue that human exams are a more suitable means of evaluating general intelligenc…
Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction
Yew Ken Chia, Hui Chen, Wei Han +4
Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in sentiment analysis, aiming to provide fine-grained insights into human sentiments. However, existing benchmarks…
Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources
Xingxuan Li, Ruochen Zhao, Yew Ken Chia +4
We present chain-of-knowledge (CoK), a novel framework that augments large language models (LLMs) by dynamically incorporating grounding information from heterogeneous sources. It…
Can ChatGPT-like Generative Models Guarantee Factual Accuracy? On the Mistakes of New Generation Search Engines
Ruochen Zhao, Xingxuan Li, Yew Ken Chia +2
Although large conversational AI models such as OpenAI's ChatGPT have demonstrated great potential, we question whether such models can guarantee factual accuracy. Recently, techno…
Is GPT-3 a Good Data Annotator?
Bosheng Ding, Chengwei Qin, Linlin Liu +4
Data annotation is the process of labeling data that could be used to train machine learning models. Having high-quality annotation is crucial, as it allows the model to learn the…
A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach
Yew Ken Chia, Lidong Bing, Sharifah Mahani Aljunied +2
Relation extraction has the potential for large-scale knowledge graph construction, but current methods do not consider the qualifier attributes for each relation triplet, such as…
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction
Yew Ken Chia, Lidong Bing, Soujanya Poria +1
Despite the importance of relation extraction in building and representing knowledge, less research is focused on generalizing to unseen relations types. We introduce the task sett…
Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction
Lu Xu, Yew Ken Chia, Lidong Bing
Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion te…
Red Dragon AI at TextGraphs 2020 Shared Task: LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking
Yew Ken Chia, Sam Witteveen, Martin Andrews
Explainable question answering for science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. To counter the limitations of metho…
Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation
Yew Ken Chia, Sam Witteveen, Martin Andrews
The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions. Red Dragon AI's e…
Scene Graph Parsing by Attention Graph
Martin Andrews, Yew Ken Chia, Sam Witteveen
Scene graph representations, which form a graph of visual object nodes together with their attributes and relations, have proved useful across a variety of vision and language appl…
Transformer to CNN: Label-scarce distillation for efficient text classification
Yew Ken Chia, Sam Witteveen, Martin Andrews
Significant advances have been made in Natural Language Processing (NLP) modelling since the beginning of 2018. The new approaches allow for accurate results, even when there is li…