DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues | Paper Citation Record

Observation 67af86c5-852a-4eab-9f36-fc04dcc5593f · outbound

This paper cites Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks

Reference 1

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Observation fb98c640-b9ef-4d10-b882-d412b61048b5 · outbound

This paper cites Phi-4 Technical Report.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Phi-4 Technical Report

Reference 2

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Observation 1263e47b-b410-4cc9-862a-b4c46cd4ebdd · outbound

This paper cites Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM: A Unified Conversational Agentic Language Model.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM: A Unified Conversational Agentic Language Model

Reference 3

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Observation ace2d624-af41-4043-99fc-7f8c256c5689 · outbound

This paper cites API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs

Reference 4

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DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Unresolved cited work

Reference 5

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Observation ee157638-d7a2-4999-a40a-7d55726c6ca0 · outbound

This paper cites Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:02:43.088698Z

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Observation 6512ffbd-b7dd-481e-88e9-49d88c10eb49 · outbound

This paper cites T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step

Reference 7

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Observation eaa1891e-46d6-412f-9683-9fdaeac463ce · outbound

This paper cites TinyAgent: Function Calling at the Edge.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues TinyAgent: Function Calling at the Edge

Reference 8

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Observation b7820be4-1746-493c-bf47-d3385e69a83e · outbound

This paper cites ToolTalk: Evaluating Tool-Usage in a Conversational Setting.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues ToolTalk: Evaluating Tool-Usage in a Conversational Setting

Reference 9

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Observation f8a22051-a2c3-4fc7-9b9b-e797ebfe2b16 · outbound

This paper cites ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Reference 10

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Observation f7cbef17-dec1-4b55-b70c-7db2756b1bc5 · outbound

This paper cites Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP

Reference 11

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Observation 140e6b49-c3e1-4f90-99d9-53875517b35e · outbound

This paper cites CoSearchAgent: A Lightweight Collaborative Search Agent with Large Language Models.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues CoSearchAgent: A Lightweight Collaborative Search Agent with Large Language Models

Reference 12

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Observation 64c732c0-1389-4afa-8e90-b22d0b70dc3b · outbound

This paper cites Intelligent Virtual Assistants with LLM-based Process Automation.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Intelligent Virtual Assistants with LLM-based Process Automation

Reference 13

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Observation 20756517-6596-4bec-9837-d139ff5bdd0b · outbound

This paper cites MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use

Reference 14

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Observation 17b10d36-0521-4cdb-9bc0-99c1e690b5a6 · outbound

This paper cites An LLM Benchmark for Addressee Recognition in Multi-modal Multi-party Dialogue.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues An LLM Benchmark for Addressee Recognition in Multi-modal Multi-party Dialogue

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:02:42.730281Z

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Observation 1890a7c7-d408-4896-9b3f-b6f43833978d · outbound

This paper cites Mistral 7B.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Mistral 7B

Reference 16

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DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Unresolved cited work

Reference 17

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Observation abb7d8da-005f-4b43-89b6-c9ed2ea968b6 · outbound

This paper cites Why and When LLM-Based Assistants Can Go Wrong: Investigating the Effectiveness of Prompt-Based Interactions for Software Help-Seeking.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Why and When LLM-Based Assistants Can Go Wrong: Investigating the Effectiveness of Prompt-Based Interactions for Software Help-Seeking

Reference 18

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Observation cce720a0-115d-4a8e-88d7-e34a0c75fab4 · outbound

This paper cites SEAL: Suite for Evaluating API-use of LLMs.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues SEAL: Suite for Evaluating API-use of LLMs

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:02:42.398827Z

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Observation 6ac36575-d17c-4731-8683-e28c29e9355e · outbound

This paper cites ETHIC: Evaluating Large Language Models on Long-Context Tasks with High Information Coverage.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues ETHIC: Evaluating Large Language Models on Long-Context Tasks with High Information Coverage

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:02:42.211387Z

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Observation a4d59512-941d-4138-8afd-e64b0a39d3d4 · outbound

This paper cites API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs

Reference 21

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Observation 2f8e3684-0fda-440d-884d-98e4ea610dc6 · outbound

This paper cites ToolACE: Winning the Points of LLM Function Calling.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues ToolACE: Winning the Points of LLM Function Calling

Reference 22

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Observation 657ffe74-1b1a-4030-b055-d8fcbe58a610 · outbound

This paper cites G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment

Reference 23

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Observation 5a800961-81c6-4fc3-9866-d9d3e3ff30c3 · outbound

This paper cites Manning and Hinrich Schütze.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Manning and Hinrich Schütze

Reference 24

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Observation f0c6393b-284a-4b21-9170-17b734c0e26e · outbound

This paper cites GPT-4 Technical Report.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues GPT-4 Technical Report

Reference 25

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This paper cites Bernstein.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Bernstein

Reference 26

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Observation ecc55d3b-2e3a-4235-9a5c-e8ae82c90c84 · outbound

This paper cites Gorilla: Large Language Model Connected with Massive APIs.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Gorilla: Large Language Model Connected with Massive APIs

Reference 27

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Observation 966f52ad-265e-4f2f-9823-e73d8ad708d8 · outbound

This paper cites Tool Learning with Foundation Models.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Tool Learning with Foundation Models

Reference 28

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This paper cites ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

Reference 29

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This paper cites Tool Learning with Large Language Models: A Survey.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Tool Learning with Large Language Models: A Survey

Reference 30

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DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Qwen2.5 Technical Report

Reference 31

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DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Unresolved cited work

Reference 32

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DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Unresolved cited work

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This paper cites Bridging HCI and AI Research for the Evaluation of Conversational SE Assistants.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Bridging HCI and AI Research for the Evaluation of Conversational SE Assistants

Reference 34

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This paper cites Toolformer: Language Models Can Teach Themselves to Use Tools.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Toolformer: Language Models Can Teach Themselves to Use Tools

Reference 36

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DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Unresolved cited work

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This paper cites TaskBench: Benchmarking Large Language Models for Task Automation.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues TaskBench: Benchmarking Large Language Models for Task Automation

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This paper cites ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases

Reference 39

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DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Language Models are Few-Shot Learners

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DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Unresolved cited work

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DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues LLaMA: Open and Efficient Foundation Language Models

Reference 42

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DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues GPTVoiceTasker: Advancing Multi-step Mobile Task Efficiency Through Dynamic Interface Exploration and Learning

Reference 43

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DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Unresolved cited work

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DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Unresolved cited work

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DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues MTU-Bench: A Multi-granularity Tool-Use Benchmark for Large Language Models

Reference 47

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DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Unresolved cited work

Reference 48

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Observation 5321c14a-8d40-4db5-a641-3352980a350c · outbound

This paper cites On the Tool Manipulation Capability of Open-source Large Language Models.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues On the Tool Manipulation Capability of Open-source Large Language Models

Reference 49

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This paper cites ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios

Reference 50

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Observation 14228d02-f6a2-4b96-bd90-60acba2b90c2 · outbound

This paper cites RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning

Reference 51

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Observation 23c8b628-30fe-47e5-a5ad-8bcf7a1084c4 · outbound

This paper cites Schweitzer, and Alison Wood Brooks.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues Schweitzer, and Alison Wood Brooks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:02:43.458492Z

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Observation 381dc53e-59e5-4fd9-b493-50ebe7b64b8d · outbound

This paper cites A Survey on Multi-Turn Interaction Capabilities of Large Language Models.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues A Survey on Multi-Turn Interaction Capabilities of Large Language Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T22:02:40.944596Z

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Observation 3e5d6cd4-2439-4fcc-9d6c-a6c20b2f61bf · outbound

This paper cites ToolQA: A Dataset for LLM Question Answering with External Tools.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues ToolQA: A Dataset for LLM Question Answering with External Tools

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T22:02:40.986835Z

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Unavailable: canonical work link unavailable.

Observation e7d363ed-31e3-499e-9178-35297422add4 · outbound

This paper cites online" 'onlinestring :=.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues online" 'onlinestring :=

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T22:02:41.035264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c7cba1e-c6ed-4806-8dfb-91e3dfc4747b · outbound

This paper cites write newline.

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues write newline

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T22:02:41.100115Z

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