3 citations · 4 across the 9 of their papers we have counts for
12 papers
Plan-and-Patch: Diffusion Language Models for Agentic Planning
Syamantak Kumar, Jiang Guo, Hassan Hamad +6
Planning is increasingly important for long-horizon agents, where successful execution requires coordinating subgoals, tool use, and intermediate outcomes over many steps. Yet assu…
Supplement Generation Training for Enhancing Agentic Task Performance
Young Min Cho, Daniele Bonadiman, Divya Bhargavi +8
Training large foundation models for agentic tasks is increasingly impractical due to the high computational costs, long iteration cycles, and rapid obsolescence as new models are…
A Study on Leveraging Search and Self-Feedback for Agent Reasoning
Karthikeyan K, Michelle Yuan, Elman Mansimov +6
Recent works have demonstrated that incorporating search during inference can significantly improve reasoning capabilities of language agents. Some approaches may make use of the g…
FLAP: Flow-Adhering Planning with Constrained Decoding in LLMs
Shamik Roy, Sailik Sengupta, Daniele Bonadiman +2
Planning is a crucial task for agents in task oriented dialogs (TODs). Human agents typically resolve user issues by following predefined workflows, decomposing workflow steps into…
Eliciting Better Multilingual Structured Reasoning from LLMs through Code
Bryan Li, Tamer Alkhouli, Daniele Bonadiman +2
The development of large language models (LLM) has shown progress on reasoning, though studies have largely considered either English or simple reasoning tasks. To address this, we…
DeAL: Decoding-time Alignment for Large Language Models
James Y. Huang, Sailik Sengupta, Daniele Bonadiman +6
Large Language Models (LLMs) are nowadays expected to generate content aligned with human preferences. Current work focuses on alignment at model training time, through techniques…
Injecting Domain Knowledge in Language Models for Task-Oriented Dialogue Systems
Denis Emelin, Daniele Bonadiman, Sawsan Alqahtani +2
Pre-trained language models (PLM) have advanced the state-of-the-art across NLP applications, but lack domain-specific knowledge that does not naturally occur in pre-training data.…
DFEE: Interactive DataFlow Execution and Evaluation Kit
Han He, Song Feng, Daniele Bonadiman +2
DataFlow has been emerging as a new paradigm for building task-oriented chatbots due to its expressive semantic representations of the dialogue tasks. Despite the availability of a…
A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection
Daniele Bonadiman, Alessandro Moschitti
An essential task of most Question Answering (QA) systems is to re-rank the set of answer candidates, i.e., Answer Sentence Selection (A2S). These candidates are typically sentence…
Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning
Daniele Bonadiman, Anjishnu Kumar, Arpit Mittal
The goal of a Question Paraphrase Retrieval (QPR) system is to retrieve equivalent questions that result in the same answer as the original question. Such a system can be used to u…
Injecting Relational Structural Representation in Neural Networks for Question Similarity
Antonio Uva, Daniele Bonadiman, Alessandro Moschitti
Effectively using full syntactic parsing information in Neural Networks (NNs) to solve relational tasks, e.g., question similarity, is still an open problem. In this paper, we prop…
Multitask Learning with Deep Neural Networks for Community Question Answering
Daniele Bonadiman, Antonio Uva, Alessandro Moschitti
In this paper, we developed a deep neural network (DNN) that learns to solve simultaneously the three tasks of the cQA challenge proposed by the SemEval-2016 Task 3, i.e., question…