Semanlink

Tags:

2023-07-14 About

Alex Olshevsky sur Twitter : "I want to argue that GPT-4 understands, at least sometimes."

Tags:

2023-04-04 About

David Chalmers sur Twitter : "what are some new and interesting results about the relative capacities of multimodal models and pure language models... (thinking about "do language models need sensory grounding for meaning and understanding?".)"

Tags:

2023-03-15 About

Some remarks on Large Language Models

Tags:

> There turned out to be a phase shift somewhere between 60B parameters and 175B parameters, that made language models super impressive.

> **The performance of current days language models are not obtained by language modeling**
>
>    - [Traditional] LMs are not [grounded](tag:grounded_language_learning)
> 
> **3 conceptual steps between GPT-3 and chatGPT: Instructions, code, RLHF.** The last one is, I think, the least interesting despite getting the most attention
>
> Instruction tuning: For example, the human annotators would write something like "please summarize this text", followed by some text they got, followed by a summary they produced of this text. -> Some symbols ("summarize", "translate", "formal") are used in a consistent way together with the concept/task they denote. And they always appear in the beginning of the text. -> the act of producing a summary grounded to the human concept of "summary"
>
> code: programming language code data, and specifically data that contains both natural language instructions or descriptions (in the form of code comments) and the corresponding programming language code. This produced another very direct form of grounding. the human language describes concepts (or intents), which are then realized in the form of the corresponding programs.
>
> "[RL with Human Feedback](tag:reinforcement_learning_from_human_feedback)". This is a fancy way of saying that the model now observes two humans in a conversation, one playing the role of a user, and another playing the role of "the AI", demonstrating how the AI should respond in different situations. This clearly helps the model learn how dialogs work, and how to keep track of information across dialog states (something that is very hard to learn from just "found" data).

2023-01-03 About

Christopher Manning sur Twitter : "As the abilities of large pre-trained language models continue to rapidly improve, as seen in this week’s ChatGPT, I find it a rather implausible position to think that these models have no understanding of the meaning of texts."

Tags:

2022-12-03 About

[2208.11857] Shortcut Learning of Large Language Models in Natural Language Understanding: A Survey

Tags:

2022-08-27 About

[2207.06300] Re2G: Retrieve, Rerank, Generate

Tags:

2022-07-14 About

[SAIF2020] Day2: Natural Language Processing - Christopher Manning | Samsung - YouTube

Tags:

2022-06-15 About

[2108.13934] Robust Retrieval Augmented Generation for Zero-shot Slot Filling

Tags:

2022-01-19 About

Multi-domain Multilingual Question Answering

Tags:

2021-12-08 About

[2107.12708] QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension

Tags:

2021-08-06 About

Agostina Calabrese sur Twitter : Structured Sentiment Analysis as Dependency Graph Parsing

Tags:

2021-08-02 About

[2104.10809] Provable Limitations of Acquiring Meaning from Ungrounded Form: What will Future Language Models Understand?

Tags:

2021-05-23 About

Learning to Tag OOV Tokens by Integrating Contextual Representation and Background Knowledge (ACL Anthology 2020)

Tags:

Aim to leverage both contextual representation of input text (deep LMs)  and  knowledge derived
from curated KBs ([Wordnet](tag:wordnet)) to improve [slot tagging](tag:slot_tagging) in the presence of [out-of-vocab](tag:oov) words ([few-shot scenario](tag:few_shot_learning))

Method:

1. retrieve potentially relevant KB entities and
encode them into distributed representations that
describe global graph-structured information
2. BERT encoder
layer to capture context-aware representations of
the sequence and attend to the KB embeddings
using multi-level graph attention
3. integrate
BERT embeddings and the KB embeddings
to predict the slot type

Contributions:

1. feasibility of applying lexical ontology
to facilitate recognizing OOV words. First to consider the large-scale background
knowledge for enhancing context-aware
slot tagging models.
2. a knowledge integration mechanism that uses multi-level graph
attention to model explicit lexical relations.
3.experiments on two benchmark datasets

> our method makes a notable difference in a
scenario where samples are linguistically diverse,
and large vocab exists.

(Better improvements when using RNN than BERT, because BERT already contains a lot of background knowledge)

2020-07-04 About

[1909.04164] Knowledge Enhanced Contextual Word Representations

Tags: