Papers by Dongji Feng
Zero-Shot Multi-Label Topic Inference with Sentence Encoders and LLMs (2023.emnlp-main)
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| Challenge: | In this paper, we focus on Zero-shot approaches for inferring topics from documents where both the document and topics were never seen by a model previously. |
| Approach: | They propose to use Sentence Encoders and Large Language Models to perform a "definition-wild zero-shot topic inference" where users define or provide topics of interest in real-time. |
| Outcome: | The proposed methods outperform ChatGPT-3.5 and PaLM and Sentence-BERT on the definition-wild zero-shot topic inference task on seven datasets. |
Exploring Universal Sentence Encoders for Zero-shot Text Classification (2022.aacl-short)
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| Challenge: | Universal Sentence Encoder (USE) has gained popularity as a general-purpose sentence encoding technique. |
| Approach: | They propose to use Universal Sentence Encoder (USE) to learn a general-purpose sentence encoding technique. |
| Outcome: | The proposed technique outperforms topic-based inference in zero-shot text classification tasks. |
TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks (2023.findings-emnlp)
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| Challenge: | Recent studies have shown that conversational Large Language Models (LLMs) can perform ill-defined complex tasks with different prompt types/styles and different degrees of detail. |
| Approach: | They propose a general taxonomy that can be used to design prompts with specific properties to perform a wide range of complex tasks. |
| Outcome: | The proposed taxonomy will allow future benchmarking studies to report specific categories of prompts used as part of the study, enabling meaningful comparisons across different studies. |
LLMs as Meta-Reviewers’ Assistants: A Case Study (2025.naacl-long)
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Eftekhar Hossain, Sanjeev Kumar Sinha, Naman Bansal, R. Alexander Knipper, Souvika Sarkar, John Salvador, Yash Mahajan, Sri Ram Pavan Kumar Guttikonda, Mousumi Akter, Md. Mahadi Hassan, Matthew Freestone, Matthew C. Williams Jr., Dongji Feng, Santu Karmaker
| Challenge: | Meta-reviews are a critical step in the overall scientific peer-reviewed process, which focuses on understanding the consensus of expert opinions on a scholarly work and making informed judgments on its scientific merit. |
| Approach: | They propose to use large language models to generate a controlled multi-perspective-summary (MPS) of their opinions to help meta-reviewers better comprehend multiple experts' perspectives. |
| Outcome: | The proposed model can help meta-reviewers better comprehend multiple experts’ perspectives by generating a controlled multi-perspective-summary (MPS) of their opinions. |