Papers with AI community
Meaning Representations for Natural Languages: Design, Models and Applications (2024.lrec-tutorials)
Copied to clipboard
| Challenge: | a tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. authors propose a cutting-edge, full-day tutorial for all stakeholders in the AI community. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models (2024.naacl-demo)
Copied to clipboard
| Challenge: | Foundation models have demonstrated a great ability to achieve general human-level intelligence far beyond traditional approaches. |
| Approach: | They propose a toolkit to simplify the finetuning of general foundation models. |
| Outcome: | The proposed toolkit simplifies the domain- and task-aware finetuning of general foundation models with limited computing resources. |
Past Meets Present: Creating Historical Analogy with Large Language Models (2025.acl-long)
Copied to clipboard
Nianqi Li, Siyu Yuan, Jiangjie Chen, Jiaqing Liang, Feng Wei, Zujie Liang, Deqing Yang, Yanghua Xiao
| Challenge: | Historical analogies are important abilities that help people make decisions and understand the world. |
| Approach: | They propose a historical analogy acquisition task that uses large language models to acquire historical analogies. |
| Outcome: | The proposed method mitigates hallucinations and stereotypes when LLMs generate historical analogies. |
MEGA: Multilingual Evaluation of Generative AI (2023.emnlp-main)
Copied to clipboard
Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Mohamed Ahmed, Kalika Bali, Sunayana Sitaram
| Challenge: | Large Large Models (LLMs) have shown impressive performance on many natural language processing tasks such as language understanding, reasoning, and language generation. |
| Approach: | They present a framework for evaluating generative LLMs in the multilingual setting and provide directions for future progress in the field. |
| Outcome: | The proposed framework evaluates generative models on 16 NLP datasets across 70 typologically diverse languages and compares them to state-of-the-art non-autoregressive models. |
What Gets Echoed? Understanding the “Pointers” in Explanations of Persuasive Arguments (D19-1)
Copied to clipboard
| Challenge: | Explanations are central to everyday life, and are a topic of growing interest in the AI community. |
| Approach: | They propose a word-level prediction task to investigate how explanations selectively reuse information from what is being explained. |
| Outcome: | The proposed features have strong predictive power on the echoing of a word in an explanation, and enhance neural methods of generating explanations. |
Boosting Scientific Concepts Understanding: Can Analogy from Teacher Models Empower Student Models? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Analogical reasoning plays a critical role in human cognition, enabling us to understand new concepts by associating them with familiar ones. |
| Approach: | They propose to use free-form analogies to aid students in understanding scientific concepts . they also show that analogies generated by student LMs can improve their own performance . |
| Outcome: | The proposed model can help students understand scientific concepts, the authors show . |
Intent Mining from past conversations for Conversational Agent (2020.coling-main)
Copied to clipboard
| Challenge: | Conversational systems are of primary interest in the AI community . many commercial bot building frameworks require a collection of user utterances and corresponding intent to train an intent model. |
| Approach: | They propose an intent discovery framework that can mine a vast amount of conversational logs and generate labeled data sets for training intent models. |
| Outcome: | The proposed framework can mine conversational logs and generate labeled data sets for training intent models. |
Value FULCRA: Mapping Large Language Models to the Multidimensional Spectrum of Basic Human Value (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing work specifies values as risk criteria formulated in the AI community, e.g., fairness and privacy protection, suffering from poor clarity, adaptability and transparency. |
| Approach: | They propose a value alignment paradigm based on Schwartz's Theory of Basic Values as an instantiation and propose 'BaseAlign' to support this paradigm. |
| Outcome: | The proposed model covers existing risks and anticipates unidentified ones with a low-data set. |
How Do In-Context Examples Affect Compositional Generalization? (2023.acl-long)
Copied to clipboard
| Challenge: | In-context learning paradigms that focus on large corpus are limiting compositional generalization performance. |
| Approach: | They propose a test suite to investigate in-context compositional generalization . they propose to use examples that are structurally similar to the test case . |
| Outcome: | The proposed test suite investigates in-context compositional generalization performance . it finds that the performance can be affected by the selection of in-const examples . |