If you’re not following @darth_na Lyndon NA, you’re missing out on fantastic deep-dives on various and sundry conversational areas in SEO. This morning I was fascinated by his take on our the “generative” search results, because Google, Bing and even Twitter have their own SGE type experiences integrated, and there is no sign that these tools will be less prevalent in the future.
Advice for Optimizing for Generative (SGE)
That would make it Candidate selection, and likely on par with FS selection, and for those that do NLP, summary component selection. You might want to look up ordinate/subordinate and check the presence of verbs/adverbs.
Darth NA (Lyndon)
We’re almost into 2024. We’ve only had “Search Intent” be mainstream for 2-3 years? We’ve only had “SERP Features” being tracked a little longer! A % of people still say “LSI Keywords” instead of topical/thematically/semantically related Entities, Taxonomies, term maps…
None of it is new. Much of it has been pushed for a decade. But it takes the sector so damned long! For the SGE – it’s not the Generative bit people need to consider, it’s the candidate selection process, and there’s 20+ years of research on it!!!
There’s literally dozens of papers regarding things like Question and Answer association/alignment, (as well as generating the Q for the A, or the A for the Q). There’s even more for summarisation, esp. older papers looking at highlighting key sentences/strings.
What About Optimizing “Entities” found in SGE Results?
I asked Lyndon what about optimizing entity discoverability an document optimization for LLM assisted tools deployed for platforms with Promethean type connections to search indices? (Aka optimizing brand query results in Bard, Grok and Bing by doing offsite citation/text sentiment analysis?
If you are dealing with an Entity (as in, a proper, Named one, not generic nouns etc.), then yes – but that should be standard. And I think that’s the frustrating part here. A lot of what is being pushed – should be “the norm” to a fair degree.
Darth NA (Lyndon)
There’s literally dozens of papers regarding things like Question and Answer association/alignment, (as well as generating the Q for the A, or the A for the Q). There’s even more for summarisation, esp. older papers looking at highlighting key sentences/strings
LLM Based Scientific Papers To Read To Understand & Optimize
You can do some operator searchers, on sites like Stanford, Princeton, MIT, Carnegie etc.
site:http://princeton.edu NLP ext:pdf "question and answer"site:http://princeton.edu
NLP ext:pdf "question and answer"
- Chain of Code: Reasoning with a Language Model-Augmented Code Emulator – https://arxiv.org/abs/2312.04474 —
- Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective – https://arxiv.org/abs/2305.15408 —
- Scaling Data-Constrained Language Models – https://arxiv.org/abs/2305.16264 —
- Language to Rewards for Robotic Skill Synthesis – https://arxiv.org/abs/2306.08647 —
- Tree of Thoughts: Deliberate Problem Solving with Large Language Models – https://arxiv.org/abs/2305.10601 —
- Why think step by step? Reasoning emerges from the locality of experience – https://arxiv.org/abs/2304.03843 — Toolformer: Language Models Can Teach Themselves to Use Tools – https://arxiv.org/abs/2302.04761 — Reasoning with Language Model is Planning with World Model – https://arxiv.org/abs/2305.14992 — ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings – https://arxiv.org/abs/2305.11554 — DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models – https://arxiv.org/abs/2306.11698 — QLoRA: Efficient Finetuning of Quantized LLMs – https://arxiv.org/abs/2305.14314 — Direct Preference Optimization: Your Language Model is Secretly a Reward Model – https://arxiv.org/abs/2305.18290 — Are Emergent Abilities of Large Language Models a Mirage? – https://arxiv.org/abs/2304.15004 — Reverse Engineering Self-Supervised Learning – https://arxiv.org/abs/2305.15614 — Learning Transformer Programs – https://arxiv.org/abs/2306.01128 — OpenAssistant Conversations — Democratizing Large Language Model Alignment – https://arxiv.org/abs/2304.07327 — Privacy Auditing with One (1) Training Run – https://arxiv.org/abs/2305.08846 — Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning – https://arxiv.org/abs/2312.05230 — Large Language Models as Zero-Shot Conversational Recommenders – https://arxiv.org/abs/2308.10053 — Zephyr: Direct Distillation of LM Alignment – https://arxiv.org/abs/2310.16944 —
