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Found 16 papers, 11 papers with code
Problem Solving Through Human-AI Preference-Based Cooperation
While there is a widespread belief that artificial general intelligence (AGI) -- or even superhuman AI -- is imminent, complex problems in expert domains are far from being solved.
Language Models can Exploit Cross-Task In-context Learning for Data-Scarce Novel Tasks
We design a cross-task prompting setup with three LLMs and show that LLMs achieve significant performance improvements despite no examples from the target task in the context.
$\texttt{LM}^\texttt{2}$: A Simple Society of Language Models Solves Complex Reasoning
The solver model generates the solution to the subproblems that are then checked by the verifier module; depending upon the feedback from the verifier, the reasoning context is constructed using the subproblems and the solutions.
How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning
Despite superior reasoning prowess demonstrated by Large Language Models (LLMs) with Chain-of-Thought (CoT) prompting, a lack of understanding prevails around the internal mechanisms of the models that facilitate CoT generation.
Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic Reasoning
In this paper, we start with the hypothesis that much smaller LMs, which are weak at multi-step reasoning, can achieve reasonable arithmetic reasoning if arithmetic word problems are posed as a formalize-then-solve task.
Ranked #12 on
Math Word Problem Solving
on SVAMP
(using extra training data)
Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoning
Additionally, we show that DaSLaM is not limited by the solver's capabilities as a function of scale; e. g., solver LMs with diverse sizes give significant performance improvement with our solver-agnostic decomposition technique.
Ranked #6 on
Overall - Test
on JEEBench
(using extra training data)
Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment
In-context learning (ICL) unfolds as large language models become capable of inferring test labels conditioned on a few labeled samples without any gradient update.
Hatemongers ride on echo chambers to escalate hate speech diffusion
We analyze more than 32 million posts from over 6. 8 million users across three popular online social networks to investigate the interrelations between hateful behavior, information dissemination, and polarised organization mediated by echo chambers.
Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining?
Identifying argument components from unstructured texts and predicting the relationships expressed among them are two primary steps of argument mining.
Semi-supervised Stance Detection of Tweets Via Distant Network Supervision
Detecting and labeling stance in social media text is strongly motivated by hate speech detection, poll prediction, engagement forecasting, and concerted propaganda detection.
Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems
The Transformer and its variants have been proven to be efficient sequence learners in many different domains.
Incomplete Gamma Integrals for Deep Cascade Prediction using Content, Network, and Exogenous Signals
Second, there is a measurable positive correlation between the novelty of the root content (with respect to a streaming external corpus) and the relative size of the resulting cascade.
Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter
Meanwhile, to predict the retweet dynamics on Twitter, we propose RETINA, a novel neural architecture that incorporates exogenous influence using scaled dot-product attention.
Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory
Unlike previous studies which model a discussion in a static manner, in the present study, we model it as a time-varying process and solve two inter-related problems -- predict which user groups will get engaged with an ongoing discussion, and forecast the growth rate of a discussion in terms of the number of comments.
Normalyzing Numeronyms -- A NLP approach
This paper presents a method to apply Natural Language Processing for normalizing numeronyms to make them understandable by humans.
How did the discussion go: Discourse act classification in social media conversations
Our proposed model outperformed the previous one in terms of domain independence; without using platform-dependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively to predict discourse roles of comments in Reddit and Facebook discussions.
