Reasoning, a fundamental cognitive process integral to human intelligence, has garnered substantial interest within artificial intelligence.
Notably, recent studies have revealed that chain-of-thought prompting significantly enhances LLM's reasoning capabilities, which attracts widespread attention from both academics and industry.
In this paper, we systematically investigate relevant research, summarizing advanced methods through a meticulous taxonomy that offers novel perspectives.
Moreover, we delve into the current frontiers and delineate the challenges and future directions, thereby shedding light on future research.
Furthermore, we engage in a discussion about open questions.
We hope this paper serves as an introduction for beginners and fosters future research.
Resources have been made publicly available at https://github.com/zchuz/CoT-Reasoning-Survey

Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future

Grasping the concept of time is a fundamental facet of human cognition, indispensable for truly comprehending the intricacies of the world.
Previous studies typically focus on specific aspects of time, lacking a comprehensive temporal reasoning benchmark.
To address this, we propose TimeBench, a comprehensive hierarchical temporal reasoning benchmark that covers a broad spectrum of temporal reasoning phenomena.
TimeBench provides a thorough evaluation for investigating the temporal reasoning capabilities of large language models.
We conduct extensive experiments on GPT-4, LLaMA2, and other popular LLMs under various settings.
Our experimental results indicate a significant performance gap between the state-of-the-art LLMs and humans, highlighting that there is still a considerable distance to cover in temporal reasoning.
Besides, LLMs exhibit capability discrepancies across different reasoning categories.
Furthermore, we thoroughly analyze the impact of multiple aspects on temporal reasoning and emphasize the associated challenges.
We aspire for TimeBench to serve as a comprehensive benchmark, fostering research in temporal reasoning.
Code and data are available at https://github.com/zchuz/TimeBench.

TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models

In order to better understand the reason behind model behaviors (i.e., making predictions), most recent works have exploited generative models to provide complementary explanations. However, existing approaches in NLP mainly focus on ``WHY A'' rather than contrastive ``WHY A NOT B'', which is shown to be able to better distinguish confusing candidates and improve data efficiency in other research fields.In this paper, we focus on generating contrastive explanations with counterfactual examples in NLI and propose a novel \textbf{K}nowledge-\textbf{A}ware \textbf{C}ontrastive \textbf{E}xplanation generation framework (\textbf{KACE}).Specifically, we first identify rationales (i.e., key phrases) from input sentences, and use them as key perturbations for generating counterfactual examples. After obtaining qualified counterfactual examples, we take them along with original examples and external knowledge as input, and employ a knowledge-aware generative pre-trained language model to generate contrastive explanations. Experimental results show that contrastive explanations are beneficial to fit the scenarios by clarifying the difference between the predicted answer and other possible wrong ones. Moreover, we train an NLI model enhanced with contrastive explanations and achieves an accuracy of 91.9\% on SNLI, gaining improvements of 5.7\% against ETPA (``Explain-Then-Predict-Attention'') and 0.6\% against NILE (``WHY A'').

KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference

In order to facilitate natural language understanding, the key is to engage commonsense or background knowledge. However, how to engage commonsense effectively in question answering systems is still under exploration in both research academia and industry. In this paper, we propose a novel question-answering method by integrating multiple knowledge sources, i.e. ConceptNet,
Wikipedia, and the Cambridge Dictionary, to boost the performance. More concretely, we first introduce a novel graph-based iterative knowledge retrieval module, which iteratively retrieves concepts and entities related to the given question and its choices from multiple knowledge sources. Afterward, we use a pre-trained language model to encode the question, retrieved knowledge and choices, and propose an answer choice-aware attention mechanism to fuse all hidden representations of the previous modules. Finally, the linear classifier for specific tasks is used to predict the answer. Experimental results on the CommonsenseQA dataset show that our method significantly outperforms other competitive methods and achieves the new state-ofthe-art. In addition, further ablation studies demonstrate the effectiveness of our graph-based iterative knowledge retrieval module and the answer choice-aware attention module in retrieving and synthesizing background knowledge from multiple knowledge sources.

Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources

This poster session includes Main Conference posters, TACL and Demos.

In-Person Poster Session 5

poster

### Welcome!
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. Our Virtual Poster Sessions will take place online Thursday, August 22, 2024.

You are required to register for this event. **Please register [here](https://2024.aclweb.org/registration). **

If you have already registered, please check your inbox for an email from Underline granting you access to ACL 2024 content.

Please register!

ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

**Session Chair: ** Nafise Sadat Moosavi 
**Session Volunteer: ** Biddut Sarker Bijoy

7B: Semantics: Sentence-level Semantics, Textual Inference and Other areas #2

technical paper

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

Registration is now open

IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

LONG17: Question Answering

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Qianglong Chen

4

3

SHORT BIO

Presentations

Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future

TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models

KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference

Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES