---
title: "Distilling Examples into Task Instructions: Enhanced In-Context"
description: "The authors from Gong.io build the Call Playbook dataset from 50 real English sales calls lasting 30–90 minutes. 43 references."
authors: ["Guy Rotman", "Adi Kopilov", "Danit Berger Zalmanson", "Omri Allouche"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1631/"
doi: "10.18653/v1/2026.findings-acl.1631"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/rotman-2026-distilling-examples-into-task-instructions/"
---
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# Distilling Examples into Task Instructions: Enhanced In-Context Learning for Real-World B2B Conversations

**Authors:** [Guy Rotman](https://md.research.elamur.ai/free/en/authors/guy-rotman/), Adi Kopilov, Danit Berger Zalmanson and Omri Allouche · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1631/) · [PDF](https://aclanthology.org/2026.findings-acl.1631.pdf) · [DOI 10.18653/v1/2026.findings-acl.1631](https://doi.org/10.18653/v1/2026.findings-acl.1631) · [OpenAlex](https://openalex.org/W7166805905)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Few-shot in-context learning (ICL) struggles with long, multi-party B2B sales conversations. Each concatenated example adds hundreds of tokens, and performance drops as the prompt grows. The authors from Gong.io build the Call Playbook dataset from 50 real English sales calls lasting 30–90 minutes. They annotate it for five prospect-oriented sales concepts, framed as binary snippet classification.

Their framework replaces in-prompt examples with knowledge an LLM distills from a small labeled set. This knowledge takes two forms: lists of positive and negative classification criteria, and a precise task description. Across several LLMs, this cuts token usage by about 99% and raises macro-averaged AUC by up to 7% over standard ICL. Performance stays stable as more examples are distilled, while token-compression baselines lose over 9 F1 points. Because the distilled instructions are readable, humans can edit them to improve results. They can also be generated by a large model and deployed on a smaller one. This makes the approach practical for transparent, low-cost industrial classification.

## Key points
- **Call Playbook dataset:**
  - Spans five sales concepts: Pain Points, Business Goals, the Decision Making Process, the Decision Criteria used, and the Decision Makers involved.
  - Snippets are five consecutive monologues, with one monologue of overlap between neighbors.
  - Each concept has class-balanced train and test sets of 200 samples each, with no call shared between the sets.
  - Labels come from three in-house annotators under expert supervision.
  - Data and code are released at github.com/gong-io/call-playbook.
- **Variants compared:**
  - The proposed variants include Criteria-Ex and Description-Ex.
  - Baselines are plain Examples ICL, Summary-Ex, and token-level compression methods.
- **Token cost (GPT-4o):**
  - Examples costs about 236 tokens per example, reaching roughly 25K tokens at 100 examples.
  - Summary-Ex costs about 126 tokens per example.
  - Description-based prompts stay under 200 tokens and criteria-based prompts under 600.
  - Test-time processing is 70% faster for criteria methods and 57% faster for description methods.
- **Human refinement:** Five annotators edited artifacts produced by Claude Sonnet 3.7 from 25 examples. Their edits raised mean macro F1 by up to 2.65% for criteria and up to 2% for descriptions. Descriptions were easier to improve by hand.
- **Limitations:**
  - The dataset is a single, small corpus.
  - Artifact quality depends on the LLM doing the distillation.
  - The study covers only binary tasks; multi-class settings may inflate prompt size.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Guy Rotman, Adi Kopilov, Danit Berger Zalmanson and Omri Allouche. 2026. Distilling Examples into Task Instructions: Enhanced In-Context Learning for Real-World B2B Conversations. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{rotman2026distilling,
  title = {{Distilling Examples into Task Instructions: Enhanced In-Context Learning for Real-World B2B Conversations}},
  author = {Guy Rotman, Adi Kopilov, Danit Berger Zalmanson and Omri Allouche},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1631},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1631}
}
```

- [← previous Distill and Align Decomposition for Enhanced Claim Verification](https://md.research.elamur.ai/free/en/papers/magomere-2026-distill-align-decomposition-enhanced-claim/)
- [next → Distilling Large Embeddings via Hyperspherical Householder Quantization](https://md.research.elamur.ai/free/en/papers/wang-2026-distilling-large-embeddings-hyperspherical-householder/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/27/)

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*Updated 1 November 2026*