232,925 research outputs found
TPE: Towards Better Compositional Reasoning over Conceptual Tools with Multi-persona Collaboration
Large language models (LLMs) have demonstrated exceptional performance in
planning the use of various functional tools, such as calculators and
retrievers, particularly in question-answering tasks. In this paper, we expand
the definition of these tools, centering on conceptual tools within the context
of dialogue systems. A conceptual tool specifies a cognitive concept that aids
systematic or investigative thought. These conceptual tools play important
roles in practice, such as multiple psychological or tutoring strategies being
dynamically applied in a single turn to compose helpful responses. To further
enhance the reasoning and planning capability of LLMs with these conceptual
tools, we introduce a multi-persona collaboration framework: Think-Plan-Execute
(TPE). This framework decouples the response generation process into three
distinct roles: Thinker, Planner, and Executor. Specifically, the Thinker
analyzes the internal status exhibited in the dialogue context, such as user
emotions and preferences, to formulate a global guideline. The Planner then
generates executable plans to call different conceptual tools (e.g., sources or
strategies), while the Executor compiles all intermediate results into a
coherent response. This structured approach not only enhances the
explainability and controllability of responses but also reduces token
redundancy. We demonstrate the effectiveness of TPE across various dialogue
response generation tasks, including multi-source (FoCus) and multi-strategy
interactions (CIMA and PsyQA). This reveals its potential to handle real-world
dialogue interactions that require more complicated tool learning beyond just
functional tools. The full code and data will be released for reproduction
Exploiting Sentence Embedding for Medical Question Answering
Despite the great success of word embedding, sentence embedding remains a
not-well-solved problem. In this paper, we present a supervised learning
framework to exploit sentence embedding for the medical question answering
task. The learning framework consists of two main parts: 1) a sentence
embedding producing module, and 2) a scoring module. The former is developed
with contextual self-attention and multi-scale techniques to encode a sentence
into an embedding tensor. This module is shortly called Contextual
self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two
scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association
Scoring (SAS). SMS measures similarity while SAS captures association between
sentence pairs: a medical question concatenated with a candidate choice, and a
piece of corresponding supportive evidence. The proposed framework is examined
by two Medical Question Answering(MedicalQA) datasets which are collected from
real-world applications: medical exam and clinical diagnosis based on
electronic medical records (EMR). The comparison results show that our proposed
framework achieved significant improvements compared to competitive baseline
approaches. Additionally, a series of controlled experiments are also conducted
to illustrate that the multi-scale strategy and the contextual self-attention
layer play important roles for producing effective sentence embedding, and the
two kinds of scoring strategies are highly complementary to each other for
question answering problems.Comment: 8 page
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