Paper Citation Record · LEDGER

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models?

As of 10 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2506.20199.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.20199 v2

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:57:47.774388Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:57:47.635511Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T22:57:47.952451Z

Reference resolution

30 of 30 outbound references displayed

verified exact2
verified fuzzy14
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8a1bda55-476f-4180-8548-f9e2209daf2d · outbound

This paper cites How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models?.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models?

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:57:47.957617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 698cfe7b-bbd9-4e13-93d5-e44ff4e14b1c · outbound

This paper cites First, we evaluate the zero-shot baseline by providing examples in the prompt.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? First, we evaluate the zero-shot baseline by providing examples in the prompt

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:57:48.231171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ee5e29be-3c70-4db9-8927-509d13247407 · outbound

This paper cites SLT Baseline This work starts with the SLT 2024 GenSEC Challenge.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? SLT Baseline This work starts with the SLT 2024 GenSEC Challenge

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:57:48.216246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 75de904f-116d-4ea1-9a72-4c2ccf88ee6f · outbound

This paper cites Baselines - Zero-shot without Examples The results reveal different prediction behaviors between IEMOCAP and the other two datasets from Figure 5.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Baselines - Zero-shot without Examples The results reveal different prediction behaviors between IEMOCAP and the other two datasets from Figure 5

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:57:48.201509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9c510fb4-1e4b-44f2-b27b-c2c77d1bbb08 · outbound

This paper cites However, the LLM achieves the highest performance with no conversation context provided on the other two datasets, 0.576 in MELD and 0.547 in EmoryNLP.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? However, the LLM achieves the highest performance with no conversation context provided on the other two datasets, 0.576 in MELD and 0.547 in EmoryNLP

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:57:48.186210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b221e79f-366b-4c75-92c9-994efdedc3c9 · outbound

This paper cites We specifically proposed an augmented example retrieval approach to prompt the LLMs with the most coherent example to the tar- get utterance.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? We specifically proposed an augmented example retrieval approach to prompt the LLMs with the most coherent example to the tar- get utterance

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:57:48.169098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1a4b5d53-d369-484e-a605-46291e4bdf28 · outbound

This paper cites 1) Due to the restriction of GPU capacity, we chose to only experiment on Llama-3.1-8B- Instruct; more complex models should be investigated.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? 1) Due to the restriction of GPU capacity, we chose to only experiment on Llama-3.1-8B- Instruct; more complex models should be investigated

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:57:48.152896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5c94db9d-74c7-45a8-b3e3-1e4aa4074ee6 · outbound

This paper cites Deep learning,.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Deep learning,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T22:57:47.670898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c26f5558-f16e-4cad-8949-2c41540d6cb3 · outbound

This paper cites Empower typed descriptions by large language models for speech emotion recognition,.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Empower typed descriptions by large language models for speech emotion recognition,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:57:48.127428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a4d0b769-9a92-4ea1-a1f8-0d80787e3308 · outbound

This paper cites Revise, Reason, and Recognize: LLM-Based Emotion Recognition via Emotion-Specific Prompts and ASR Error Correction.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Revise, Reason, and Recognize: LLM-Based Emotion Recognition via Emotion-Specific Prompts and ASR Error Correction

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:57:47.936765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0d9c2bdd-1b71-4288-92b2-15f9961718ff · outbound

This paper cites Enhancing multimodal emo- tion recognition through asr error compensation and llm fine- tuning,.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Enhancing multimodal emo- tion recognition through asr error compensation and llm fine- tuning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:57:48.113056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 40fc22d4-c0b4-4a3c-b635-a3a0c7db320a · outbound

This paper cites Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T22:57:47.690388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95184bf4-83d8-435b-9c34-75e1212962da · outbound

This paper cites Foundation model assisted automatic speech emotion recognition: Transcribing, annotating, and aug- menting,.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Foundation model assisted automatic speech emotion recognition: Transcribing, annotating, and aug- menting,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:57:48.098906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 55573e7d-751c-4aaf-9737-94e6b874ef6d · outbound

This paper cites Large language model based generative error correction: A challenge and base- lines for speech recognition, speaker tagging, and emotion recog- nition,.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Large language model based generative error correction: A challenge and base- lines for speech recognition, speaker tagging, and emotion recog- nition,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:57:48.083504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fcfb82b1-9910-405b-988d-52e6f5198473 · outbound

This paper cites Iemocap: Interactive emotional dyadic motion capture database,.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Iemocap: Interactive emotional dyadic motion capture database,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T22:57:47.704266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d704c22-2375-4045-865c-fcd9292121b6 · outbound

This paper cites MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T22:57:47.708657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e1a58d5-fcb5-4f6a-9c8f-47e5f2b718c2 · outbound

This paper cites Emotion detection on tv show tran- scripts with sequence-based convolutional neural networks,.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Emotion detection on tv show tran- scripts with sequence-based convolutional neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:57:48.057276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a087e5a0-4234-4853-aed2-8509c93ce075 · outbound

This paper cites Large language model-based emotional speech annotation using context and acoustic feature for speech emotion recognition,.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Large language model-based emotional speech annotation using context and acoustic feature for speech emotion recognition,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:57:48.041237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 07eaba98-3561-4d71-8e8d-60286c835217 · outbound

This paper cites The Llama 3 Herd of Models.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? The Llama 3 Herd of Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T22:57:47.721877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91180e31-9855-4e9d-b8de-4f661ec3836f · outbound

This paper cites Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T22:57:47.726992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 604aa43e-8243-4637-9642-561556588e70 · outbound

This paper cites Rethinking the role of demonstra- tions: What makes in-context learning work?.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Rethinking the role of demonstra- tions: What makes in-context learning work?

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:57:48.026021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fd3ab1c5-ded0-4cc8-8e82-872f8a281b19 · outbound

This paper cites What Makes Good In-Context Examples for GPT-$3$?.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? What Makes Good In-Context Examples for GPT-$3$?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T22:57:47.736404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e14ab41-5bfa-4d38-9c16-95b40a2b8cad · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T22:57:47.741320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9094b1cd-5850-4723-86e9-365c3f594bb5 · outbound

This paper cites Mistral 7b,.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Mistral 7b,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T22:57:47.745881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c0540a3-6100-4499-b818-23d775621cdd · outbound

This paper cites Hubert: Self-supervised speech represen- tation learning by masked prediction of hidden units,.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Hubert: Self-supervised speech represen- tation learning by masked prediction of hidden units,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T22:57:47.755400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 497738fa-6a4c-4c63-8fc9-0cf91cff69f5 · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T22:57:47.759989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08aa9898-cef1-4475-956f-7274a0c09286 · outbound

This paper cites Robust speech recognition via large-scale weak supervision,.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Robust speech recognition via large-scale weak supervision,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T22:57:47.764948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77b563b2-9072-4c58-8b5e-888bac879210 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Gemma 2: Improving Open Language Models at a Practical Size

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T22:57:47.769525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05b74020-7f27-4d25-bcf3-f222ba879013 · outbound

This paper cites Msp-improv: An acted corpus of dyadic interactions to study emotion perception,.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Msp-improv: An acted corpus of dyadic interactions to study emotion perception,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:57:47.973702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 12a9a385-84b2-4167-86f2-0da0ca92435b · outbound

This paper cites Mistral 7B.

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models? Mistral 7B

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T22:57:47.750703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 8a1bda55-476f-4180-8548-f9e2209daf2d · inbound