Renjie Pi, Jiahui Gao, Shizhe Diao, Rui Pan, Hanze Dong, Jipeng Zhang, Lewei Yao, Jianhua Han, Hang Xu, Lingpeng Kong, Tong Zhang
| Challenge: | Recent advances in the field of computer vision have enabled more effective and sophisticated interactions between humans and machines. |
| Approach: | They propose a reasoning-based object detection paradigm that leverages state-of-the-art multi-modal models and open-vocabulary object detectors to perform reasoning within the context of the user’s instructions and the visual scene. |
| Outcome: | The proposed method enables users to interact with the system using natural language instructions, allowing for a higher level of interactivity. |
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Object Counts! Bringing Explicit Detections Back into Image Captioning (N18-1)
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| Challenge: | Existing approaches to image captioning use explicit object detectors as an intermediate step, but they bypass the explicit detection phase and instead generate captions directly from image embeddings. |
| Approach: | They argue that explicit detections provide rich semantic information and can thus be used as an interpretable representation to better understand why end-to-end image captioning systems work well. |
| Outcome: | The proposed methods can be used to understand why end-to-end captioning systems work well. |
COFAR: Commonsense and Factual Reasoning in Image Search (2022.aacl-main)
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Prajwal Gatti, Abhirama Subramanyam Penamakuri, Revant Teotia, Anand Mishra, Shubhashis Sengupta, Roshni Ramnani
| Challenge: | Existing approaches to retrieve relevant images for natural language searches are limited by visual recognition and lack of commonsense reasoning. |
| Approach: | They propose a framework that leverages visual content and natural language queries to enable commonsense reasoning and factual reasoning in the image search. |
| Outcome: | The proposed framework enables commonsense and factual reasoning in image search on a COFAR dataset. |
Towards Reasoning in Large Language Models: A Survey (2023.findings-acl)
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| Challenge: | Reasoning is a fundamental aspect of human intelligence that plays a crucial role in many intellectual activities. |
| Approach: | They propose to improve LLMs' ability to elicit reasoning by providing exemplars or prompts to model reasoning. |
| Outcome: | This paper provides a comprehensive overview of the state of knowledge on reasoning in large language models. |
StructGPT: A General Framework for Large Language Model to Reason over Structured Data (2023.emnlp-main)
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| Challenge: | Experiments conducted on three types of structured data show that StructGPT greatly improves the performance of LLMs. |
| Approach: | They propose an iterative Reading-then-Reasoning framework to solve question answering tasks based on structured data. |
| Outcome: | The proposed framework improves the reasoning ability of large language models over structured data under the few-shot and zero-shot settings. |
Empirical Study of Zero-Shot NER with ChatGPT (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have been a key component of natural language processing (NLP) . |
| Approach: | They propose to decompose the NER task into simpler subproblems by labels and propose a syntactic augmentation strategy to stimulate model's intermediate thinking. |
| Outcome: | The proposed methods achieve remarkable improvements for zero-shot NER across seven benchmarks, including Chinese and English datasets. |
Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models (2024.findings-acl)
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| Challenge: | Object hallucination has been an Achilles’ heel which hinders the broader applications of large vision-language models (LVLMs). |
| Approach: | They propose a logical closed loop-based framework for Object Hallucination Detection and Mitigation that uses logical consistency probing to raise questions with logical correlations to determine hallucinations. |
| Outcome: | The proposed method can be applied to all existing LVLMs and is effective and general. |
There’s a Time and Place for Reasoning Beyond the Image (2022.acl-long)
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| Challenge: | Currently, most work in this area is focused on reasoning with local evidence, but there is a gap between a state-of-the-art joint model and human performance. |
| Approach: | They propose a model that can be used to infer, associate, and reason with contextual information from other sources to establish a more complete picture. |
| Outcome: | The proposed model can identify the news articles related to the image through segment-wise understandings of the signs, buildings, crowds, and more. |
KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models (2023.findings-emnlp)
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| Challenge: | Using large language models for complex reasoning tasks on knowledge graphs remains unexplored. |
| Approach: | They propose a multi-purpose framework leveraging large language models for complex reasoning tasks on knowledge graphs. |
| Outcome: | The proposed framework outperforms fully-supervised models in KG-based fact verification and KGQA benchmarks. |
It is not a piece of cake for GPT: Explaining Textual Entailment Recognition in the presence of Figurative Language (2025.coling-main)
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| Challenge: | Figure-based language is used to convey opinions, ideas, or emotions in texts and dialogues. |
| Approach: | They evaluate the capabilities of Large Language Models to address TER and generate textual explanations of TER predictions. |
| Outcome: | The proposed model outperforms the open-source models in Zero- and Few-Shot Learning settings and shows significant performance improvements. |
Complex Reasoning in Natural Language (2023.acl-tutorials)
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| Challenge: | Recent research shows that pretrained language models are often brittle for complex reasoning tasks. |
| Approach: | They propose to use pre-trained language models to teach machines to reason over texts . they will review recent promising approaches to tackling complex reasoning tasks . |
| Outcome: | This tutorial reviews promising approaches to complex reasoning tasks . it reviews the methods that can be used to augment models with robustness . |