Kickoff meeting

10h00 WP0 + intro (LORIA)
10h30 WP1: finetuning (LORIA)
11h00 WP2: low-cost LLM (LIX)
14h00 WP3: LLMs for spoken dialogues (Linagora)
14h30 WP4: LLM+other data (APHP)
15h00 WP5: communication (Lionagora)
15h30 Session data: (Linagora + APHP)
16h15 Setup agenda and next meetings
16h45 Misc and wrapup


  • Participants: all

  • Website

  • PMT

  • Advisory board

  • Data Management Plan


  • https://ia.loria.fr/llm4all
  • Intranet: (see password in email from Sep 8th 2023 11:41 )
    • documents
  • gitlab: https://gitlab.inria.fr/synalp/llm4all
    • minutes, sources, website…
  • mailing list: llm4all@inria.fr
  • Suggestions? (mastodon?…)

Project Management Team

  • PMT
    • 1 person/partner + WP leader
    • shall meet every month: date / time?
  • Advisory board
    • PMT + invited external experts + ANR
    • shall meet every year

First Deliverables

  • T0+6: D0.2: Data Management Plan

  • List all data produced/consumed:

    • datasets, code, publications, internal & external reports, deliverables, website, blogs…
    • public/private, licence, diffusion, where stored, security, when deleted, how long-term support
  • Website OPIDOR? (painful!); Latex template!

WP1 “Finetuning”

  • How to finetune
  • Continual learning

Overview: LLMs

What is a Large Language Model?

  • An LLM is a transformer that transforms texts into a representation (embedding), and predicts the next word
  • Transformer invented by Google in 2017:
    • No bottleneck of information (compared to previous models)
    • It scales!

Scaling property of LLMs

  • scaling = if you add parameters, it can store more information
  • controlled by measuring its performances on tasks
  • scaling law = power law = \(y(x) = ax^{-\gamma} +b\)
  • metric = test loss
  • \(\gamma\) = slope

Baidu paper 2017

GPT3-175b paper 2020

  • emergence of In-Context Learning !
    • provide examples of a task in the context, the output mimics the examples

Scaling laws for Neural LM 2020

Open-AI 2020

  • RL, protein, chemistry…
  • Scaling exist since long in ML Paper on learning curves
  • But reducing test loss is linked to emergent abilities in transformers
    • Such emergence never been observed in ML before

Chinchilla paper 2022

  • GPT3: train on 300b tokens, and scale parameters up
  • given fixed FLOPS, optimal balance btw dataset / parameters
  • Lesson: need more data!

\(L(N)=\frac A {N^\alpha}\)

  • \(N\) = dataset size
  • \(\alpha \simeq 0.5\) (was 0.05 in GPT3 paper)

  • Any really useful enhancement of transformers since 2017?
    • FlashAttention
    • Pre-layer norm
    • Parallel feed-forward and attention
    • Rotary + Alibi positional encodings

Anthropic paper 2022

  • smooth scaling results from combination of abrupt emergences

Open-source LLM community

  • Prompters
  • Finetuners
  • Trainers (Eleuther, Meta, Anthropic, Mistral…)
  • Integrators (LangChain, Coala…)
  • Theoreticians (academics)


  • Catastrophic forgetting is linear! paper

  • LR scheduler
    • pretraining: start with large LR (big jumps), then decrease
    • finetuning starts from a “deep” optimum
    • redo big jumps? Will forget the previous optimum
    • continue small LR? Will not learn new optimum

Limiting forgetting

  • rehearsal
  • regularization from initial model
  • growing networks